Johan A. K. Suykens

Johan A. K. Suykens, Bart De Moor, Bart Baesens, 2000, KES'2000. Fourth International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies. Proceedings (Cat. No.00TH8516).

Kernel spectral document clustering using unsupervised precision-recall metrics

Johan A. K. Suykens, Raghvendra Mall, 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

Least-Squares Support Vector Machines for the Identification of Wiener-Hammerstein Systems

Johan A. K. Suykens, Bart De Moor, Kristiaan Pelckmans, 2012 .

Bayesian Least Squares Support Vector Machines for Classification of Ovarian Tumors

Sabine Van Huffel, Johan A. K. Suykens, Dirk Timmerman, 2004 .

SVD truncation schemes for fixed-size kernel models

Johan A. K. Suykens, Johan Schoukens, Anna Marconato, 2014, 2014 International Joint Conference on Neural Networks (IJCNN).

A PARTAN-accelerated Frank-Wolfe algorithm for large-scale SVM classification

Johan A. K. Suykens, Ricardo Ñanculef, Emanuele Frandi, 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

Positive semi-definite embedding for dimensionality reduction and out-of-sample extensions pdf

Johan A. K. Suykens, Jean-Charles Delvenne, Michaël Fanuel, 2017, SIAM J. Math. Data Sci..

Application of NLq Neural Control Theory to a Ball and Beam System

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 1998, Eur. J. Control.

Spatiotemporal pattern formation in the ACE16k CNN chip

Johan A. K. Suykens, Joos Vandewalle, Müstak E. Yalçin, 2005, ISCAS.

Optimal Quadrature-Sparsification for Integral Operator Approximation

Johan A. K. Suykens, Bertrand Gauthier, 2018, SIAM J. Sci. Comput..

Functional ANOVA Models: Convex-concave approach and concurvity analysis

Johan A. K. Suykens, Kristiaan Pelckmans, Marco Signoretto, 2008 .

Global asymptotic stability criteria for multilayer recurrent neural networks with applications to modelling and control

Johan A. K. Suykens, Joos Vandewalle, 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

LS-SVMlab Toolbox User's Guide

Johan A. K. Suykens, Joos Vandewalle, Peter Karsmakers, 2010 .

Black-box modeling for temperature prediction in weather forecasting

Johan A. K. Suykens, Siamak Mehrkanoon, Zahra Karevan, 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

Lazy learning for iterated time-series prediction

Johan A. K. Suykens, Mauro Birattari, Hugues Bersini, 1998 .

Self-tuned kernel spectral clustering for large scale networks

Johan A. K. Suykens, Rocco Langone, Raghvendra Mall, 2013, 2013 IEEE International Conference on Big Data.

Multiclass least squares support vector machines

Johan A. K. Suykens, Joos Vandewalle, J. Suykens, 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

MIMO hammerstein system identification using LS-SVM and steady state time response

Johan A. K. Suykens, Oscar Mauricio Agudelo, Ricardo Castro-Garcia, 2017, 2017 IEEE Symposium Series on Computational Intelligence (SSCI).

Linear parametric noise models for Least Squares Support Vector Machines

Johan A. K. Suykens, Bart De Moor, Tillmann Falck, 2010, 49th IEEE Conference on Decision and Control (CDC).

Brain tumor classification based on long echo proton MRS signals

Sabine Van Huffel, Johan A. K. Suykens, Carles Arús, 2004, Artif. Intell. Medicine.

Fast Hyperparameter Tuning for Support Vector Machines with Stochastic Gradient Descent

Johan A. K. Suykens, Marcin Orchel, 2020, LOD.

FURS: Fast and Unique Representative Subset selection retaining large-scale community structure

Johan A. K. Suykens, Rocco Langone, Raghvendra Mall, 2013, Social Network Analysis and Mining.

Kernel Canonical Correlation Analysis and Least Squares Support Vector Machines

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 2001, ICANN.

Coordinate Descent Algorithm for Ramp Loss Linear Programming Support Vector Machines

Johan A. K. Suykens, Shuning Wang, Xiaolin Huang, 2015, Neural Processing Letters.

Robust Constrained Linear State Feedback Synthesis using LMI's and Polyhedral Invariant Sets

Johan A. K. Suykens, Bart De Moor, Bert Pluymers, 2006 .

Coupled Simulated Annealing

Johan A. K. Suykens, Samuel Xavier de Souza, Joos Vandewalle, 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

Arne Robben, Johan A. K. Suykens, Nikolay Chumerin, 2010, 2010 IEEE International Workshop on Machine Learning for Signal Processing.

The K.U.Leuven Time Series Prediction Competition

Johan A. K. Suykens, Joos Vandewalle, J. Suykens, 1998 .

Neural networks for control

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 1996 .

Sparse LS-SVMs with L0 - norm minimization

Johan A. K. Suykens, José R. Dorronsoro, Kris De Brabanter, 2011, ESANN.

Learning with tensors: a framework based on convex optimization and spectral regularization

Johan A. K. Suykens, Lieven De Lathauwer, Dinh Quoc Tran, 2014, Machine Learning.

Joint Conference on Neural Networks , Orlando , Florida , USA , August 12-17 , 2007 ICA through an LS-SVM based Kernel CCA Measure for Independence

Johan A. K. Suykens, Carlos Alzate, J. Suykens, 2007 .

Ensemble Kernel Methods, Implicit Regularization and Determinental Point Processes pdf

Johan A. K. Suykens, Joachim Schreurs, Michael Fanuel, 2020, ArXiv.

Nosologic Imaging of Brain Tumors Using MRI and MRSI

Sabine Van Huffel, Johan A. K. Suykens, Dirk Vandermeulen, 2011 .

On the realization of n-scroll attractors

Johan A. K. Suykens, Joos Vandewalle, Müstak E. Yalçin, 1999, ISCAS'99. Proceedings of the 1999 IEEE International Symposium on Circuits and Systems VLSI (Cat. No.99CH36349).

Sparse Kernel Regression with Coefficient-based $\ell_q-$regularization

Johan A. K. Suykens, Lei Shi, Xiaolin Huang, 2019, J. Mach. Learn. Res..

Artificial neural networks: architectures and learning rules

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 1996 .

Chaotic systems synchronization

Johan A. K. Suykens, Joos Vandewalle, Mustak E. Yalcin, 2003 .

LS-SVMlab Toolbox User's Guide version 1.7

Johan A. K. Suykens, Bart De Moor, Peter Karsmakers, 2003 .

Partial Synchronization in oscillator Arrays with Asymmetric Coupling

Johan A. K. Suykens, Joos Vandewalle, Serkan Günel, 2007, Int. J. Bifurc. Chaos.

Reweighted l1 Dual Averaging Approach for Sparse Stochastic Learning

Johan A. K. Suykens, Vilen Jumutc, J. Suykens, 2014, ESANN.

Robust Cross-Validation Score Function for Non-linear Function Estimation

Johan A. K. Suykens, Joos Vandewalle, Kristiaan Pelckmans, 2002, ICANN.

Deep convolutional learning for general early design stage prediction models

Johan A. K. Suykens, Philipp Geyer, Sundaravelpandian Singaravel, 2019, Adv. Eng. Informatics.

Support Vector Machines: A Nonlinear Modelling and Control Perspective

Johan A. K. Suykens, J. Suykens, 2001, Eur. J. Control.

Constrained linear MPC with time-varying terminal cost using convex combinations

Johan A. K. Suykens, Bart De Moor, Bert Pluymers, 2005, Autom..

Hierarchical semi-supervised clustering using KSC based model

Johan A. K. Suykens, Raghvendra Mall, Siamak Mehrkanoon, 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

A mixed effects least squares support vector machine model for classification of longitudinal data

Sabine Van Huffel, Johan A. K. Suykens, Geert Molenberghs, 2012, Comput. Stat. Data Anal..

Spatio-temporal Stacked LSTM for Temperature Prediction in Weather Forecasting pdf

Johan A. K. Suykens, Zahra Karevan, J. Suykens, 2018, ArXiv.

Robust Low-Rank Tensor Recovery With Regularized Redescending M-Estimator

Johan A. K. Suykens, Yunlong Feng, Yuning Yang, 2016, IEEE Transactions on Neural Networks and Learning Systems.

Sequentially activated groups in neural networks

Johan A. K. Suykens, Grigory V. Osipov, Maxim Komarov, 2009 .

Fixed-Size Least Squares Support Vector Machines: Scala Implementation for Large Scale Classification

Johan A. K. Suykens, Bart De Moor, Raghvendra Mall, 2015, 2015 IEEE Symposium Series on Computational Intelligence.

Sparse least squares Support Vector Machine classifiers

Johan A. K. Suykens, Joos Vandewalle, Lukas Lukas, 2000, ESANN.

Learning Tensors in Reproducing Kernel Hilbert Spaces with Multilinear Spectral Penalties pdf

Johan A. K. Suykens, Lieven De Lathauwer, Marco Signoretto, 2013, ArXiv.

Learning of spatiotemporal behaviour in cellular neural networks: Research Articles

Johan A. K. Suykens, Joos Vandewalle, Samuel Xavier-de-Souza, 2006 .

Least squares support vector machines for classification and nonlinear modelling

Johan A. K. Suykens, J. Suykens, 2000 .

Prediction of mental development of preterm newborns at birth time using LS-SVM

Sabine Van Huffel, Johan A. K. Suykens, Chuan Lu, 2002, ESANN.

Nonlinear H$_{\infty}$ control for continuous-time recurrent neural networks

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 1997 .

Correction to "Gradient-Based Manipulation of Nonparametric Entropy Estimates''

Johan A. K. Suykens, Joos Vandewalle, B. De Moor, 2007 .

Generalization Properties of hyper-RKHS and its Application to Out-of-Sample Extensions pdf

Johan A. K. Suykens, Lei Shi, Jie Yang, 2018, ArXiv.

Prediction Intervals for NAR Model Structures Using a Bootstrap Method

Johan A. K. Suykens, Joseph De Brabanter, Bart De Moor, 2005 .

Clustering from two data sources using a kernel-based approach with weight coupling

Johan A. K. Suykens, Rocco Langone, Lynn Houthuys, 2016, ESANN.

Chaos Synchronization: a Lagrange Programming Network Approach

Johan A. K. Suykens, Joos Vandewalle, 2000, Int. J. Bifurc. Chaos.

Fast Prediction with SVM Models Containing RBF Kernels pdf

Johan A. K. Suykens, Bart De Moor, Marc Claesen, 2014, ArXiv.

A Risk Minimization Principle for a Class of Parzen Estimators

Johan A. K. Suykens, Bart De Moor, Kristiaan Pelckmans, 2007, NIPS.

The skweezee system: enabling the design and the programming of squeeze interactions

Luc Geurts, Johan A. K. Suykens, Vero Vanden Abeele, 2013, UIST.

Systematic benchmarking of microarray data classification: assessing the role of non-linearity and dimensionality reduction

Johan A. K. Suykens, Bart De Moor, Frank De Smet, 2004, Bioinform..

Support vector methods for survival analysis: a comparison between ranking and regression approaches

Sabine Van Huffel, Vanya Van Belle, Johan A. K. Suykens, 2011, Artif. Intell. Medicine.

Supervised Novelty Detection

Johan A. K. Suykens, Vilen Jumutc, 2013, 2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM).

Nonparametric Regression via StatLSSVM

Johan A. K. Suykens, Bart De Moor, Kris De Brabanter, 2013 .

Supervised aggregated feature learning for multiple instance classification

Johan A. K. Suykens, Rocco Langone, J. Suykens, 2017, Inf. Sci..

Incremental kernel spectral clustering for online learning of non-stationary data

Johan A. K. Suykens, Bart De Moor, Rocco Langone, 2014, Neurocomputing.

Very Sparse LSSVM Reductions for Large-Scale Data

Johan A. K. Suykens, Raghvendra Mall, J. Suykens, 2015, IEEE Transactions on Neural Networks and Learning Systems.

Handling missing values in support vector machine classifiers

Johan A. K. Suykens, Bart De Moor, Kristiaan Pelckmans, 2005, Neural Networks.

Highly sparse kernel spectral clustering with predictive out-of-sample extensions

Johan A. K. Suykens, Carlos Alzate, 2010, ESANN.

Learning with the maximum correntropy criterion induced losses for regression

Johan A. K. Suykens, Lei Shi, Xiaolin Huang, 2015, J. Mach. Learn. Res..

LS-SVMlab : a MATLAB / C toolbox for Least Squares Support Vector Machines

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 2007 .

Identification of MIMO Hammerstein models using least squares support vector machines

Johan A. K. Suykens, Bart De Moor, Kristiaan Pelckmans, 2005, Autom..

Application of a Smoothing Technique to Decomposition in Convex Optimization

Johan A. K. Suykens, Ion Necoara, J. Suykens, 2008, IEEE Transactions on Automatic Control.

A regularized formulation for spectral clustering with pairwise constraints

Johan A. K. Suykens, Carlos Alzate, J. Suykens, 2009, 2009 International Joint Conference on Neural Networks.

Scroll maps from n-scroll attractors

Johan A. K. Suykens, Joos Vandewalle, Serdar Ozoguz, 2002 .

Tensor Versus Matrix Completion: A Comparison With Application to Spectral Data

Johan A. K. Suykens, Bart De Moor, Marco Signoretto, 2011, IEEE Signal Processing Letters.

Basic Methods of Least Squares Support Vector Machines

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 2002 .

Coupled chaotic simulated annealing processes

Johan A. K. Suykens, Joos Vandewalle, Müstak E. Yalçin, 2003, Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS '03..

Least Squares Support Vector Machines

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 2002 .

Kernelized Elastic Net Regularization: Generalization Bounds, and Sparse Recovery

Johan A. K. Suykens, Yunlong Feng, Hanyuan Hang, 2016, Neural Computation.

CNNs, Intelligence and Synchronization Theory

Johan A. K. Suykens, Joos Vandewalle, J. Suykens, 2001 .

Classification With Truncated $\ell _{1}$ Distance Kernel

Johan A. K. Suykens, Andreas Maier, Shuning Wang, 2018, IEEE Transactions on Neural Networks and Learning Systems.

Kernel Clustering for Knowledge Discovery in Clinical Microarray Data Analysis

Johan A. K. Suykens, Tijl De Bie, Bart De Moor, 2007 .

Robust NL/sub q/ neural control theory

Johan A. K. Suykens, Bart De Moor, Joos Vandewalle, 1997, Proceedings of International Conference on Neural Networks (ICNN'97).

Phenomena from CNNs with generalized Chua's circuits

Leon O. Chua, Johan A. K. Suykens, Joos Vandewalle, 1997 .

Morozov, Ivanov and Tikhonov Regularization Based LS-SVMs

Johan A. K. Suykens, Bart De Moor, Kristiaan Pelckmans, 2004, ICONIP.

Fixed-size kernel logistic regression for phoneme classification

Johan A. K. Suykens, Hugo Van hamme, Peter Karsmakers, 2007, INTERSPEECH.

Financial time series prediction using least squares support vector machines within the evidence framework

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 2001, IEEE Trans. Neural Networks.

Automatic relevance determination for Least Squares Support Vector Machines classifiers

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 2001, ESANN.

Impulsive synchronization of Lur'e systems: state feedback case

Leon O. Chua, Johan A. K. Suykens, Joos Vandewalle, 1998 .

Generative Restricted Kernel Machines: A framework for multi-view generation and disentangled feature learning

Johan A. K. Suykens, Joachim Schreurs, Arun Pandey, 2020, Neural Networks.

Robustness of reweighted Least Squares Kernel Based Regression

Johan A. K. Suykens, Andreas Christmann, Mia Hubert, 2010, J. Multivar. Anal..

Large Scale Problems

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 2002 .

Kernel spectral clustering for predicting maintenance of industrial machines

Johan A. K. Suykens, Bart De Ketelaere, Rocco Langone, 2013, 2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM).

Transductive Feature Selection Using Clustering-Based Sample Entropy for Temperature Prediction in Weather Forecasting

Johan A. K. Suykens, Zahra Karevan, 2018, Entropy.

Incorporating Best Linear Approximation within LS-SVM-based Hammerstein System Identification

Johan A. K. Suykens, Johan Schoukens, Koen Tiels, 2015, 2015 54th IEEE Conference on Decision and Control (CDC).

Feature Selection in Survival Least Squares Support Vector Machines with Maximal Variation Constraints

Sabine Van Huffel, Vanya Van Belle, Johan A. K. Suykens, 2009, IWANN.

An improved dual decomposition approach to DSL dynamic spectrum management

Johan A. K. Suykens, Paschalis Tsiaflakis, Marc Moonen, 2009, 2009 17th European Signal Processing Conference.

Intelligence and Cooperative Search by Coupled Local Minimizers

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 2002, Int. J. Bifurc. Chaos.

Higher order Matching Pursuit for Low Rank Tensor Learning pdf

Johan A. K. Suykens, Siamak Mehrkanoon, Yuning Yang, 2015, ArXiv.

EnsembleSVM: a library for ensemble learning using support vector machines pdf

Johan A. K. Suykens, Bart De Moor, Marc Claesen, 2014, J. Mach. Learn. Res..

Online semi-supervised clustering regularized by Kalman filtering

Johan A. K. Suykens, Siamak Mehrkanoon, Mauricio Agudelo Manozca, 2015 .

Indefinite Kernel Logistic Regression With Concave-Inexact-Convex Procedure

Johan A. K. Suykens, Chen Gong, Jie Yang, 2019, IEEE Transactions on Neural Networks and Learning Systems.

Efficient adaptive filtering for smooth linear FIR models

Johan A. K. Suykens, Toon van Waterschoot, Kristiaan Pelckmans, 2010, 2010 18th European Signal Processing Conference.

Nonlinear H/sub /spl infin// synchronization: case study for a hyperchaotic system

Leon O. Chua, Johan A. K. Suykens, J. Vandewalle, 1998, ISCAS '98. Proceedings of the 1998 IEEE International Symposium on Circuits and Systems (Cat. No.98CH36187).

Identifying intervals for hierarchical clustering using the Gershgorin circle theorem

Johan A. K. Suykens, Raghvendra Mall, Siamak Mehrkanoon, 2015, Pattern Recognit. Lett..

Classification of Ovarian Tumors Using Bayesian Least Squares Support Vector Machines

Sabine Van Huffel, Johan A. K. Suykens, Dirk Timmerman, 2003, AIME.

Fast and roust face tracking applieed to wheelchair driving

Johan A. K. Suykens, Joos Vandewalle, Samuel Xavier-de-Souza, 2006 .

Learning Partial Synchronization Regimes with Imposed Qualitative Behavior on an Array of Chua's Oscillators

Johan A. K. Suykens, Joos Vandewalle, Serkan Gunel, 2006 .

Least squares support vector machines classifiers : a multi two-spiral benchmark problem

Johan A. K. Suykens, Joos Vandewalle, L. Lukas, 2001 .

Kernel spectral clustering for dynamic data using multiple kernel learning

Johan A. K. Suykens, Germán Castellanos-Domínguez, Diego Hernán Peluffo-Ordóñez, 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

Classification of ovarian tumor using Bayesian least squares support vector machines

Sabine Van Huffel, Johan A. K. Suykens, Dirk Timmerman, 2003 .

Fast primal-dual projected linear iterations for distributed consensus in constrained convex optimization

Johan A. K. Suykens, Ion Necoara, Ioan Dumitrache, 2010, 49th IEEE Conference on Decision and Control (CDC).

Optimized Data Fusion for Kernel k-Means Clustering

Johan A. K. Suykens, Bart De Moor, Shi Yu, 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Feature Extraction and Classification of EEG Signals for Rapid P300 Mind Spelling

Johan A. K. Suykens, Marc M. Van Hulle, Nikolay Chumerin, 2009, 2009 International Conference on Machine Learning and Applications.

Sparse Reductions for Fixed-Size Least Squares Support Vector Machines on Large Scale Data

Johan A. K. Suykens, Raghvendra Mall, 2013, PAKDD.

Least Squares Support Vector Machines for Modeling Electronic Devices

Johan A. K. Suykens, Peter Karsmakers, Kristiaan Pelckmans, 2008 .

Primal-Dual Framework for Feature Selection using Least Squares Support Vector Machines

Johan A. K. Suykens, Raghvendra Mall, Halima Bensmail, 2013, COMAD.

P300 Detection Based on Feature Extraction in On-line Brain-Computer Interface

Johan A. K. Suykens, Refet Firat Yazicioglu, Chris Van Hoof, 2009, KI.

Experimental observation of autowaves on the ACE16k CNN chip

Johan A. K. Suykens, Joos Vandewalle, Mustak E. Yalcin, 2004 .

Unsupervised learning of disentangled representations in deep restricted kernel machines with orthogonality constraints pdf

Johan A. K. Suykens, Panagiotis Patrinos, Francesco Tonin, 2020, Neural Networks.

Robustness of kernel based regression: Influence and weight functions

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

Nonconvex Proximal Splitting with Computational Errors Suvrit Sra

Johan A. K. Suykens, Marco Signoretto, Andreas Argyriou, 2014 .

First and Second Order SMO Algorithms for LS-SVM Classifiers

Johan A. K. Suykens, Jorge López Lázaro, J. Suykens, 2011, Neural Processing Letters.

Discovering Cluster Dynamics Using Kernel Spectral Methods

Johan A. K. Suykens, Joos Vandewalle, Rocco Langone, 2016 .

Learning of spatiotemporal behaviour in cellular neural networks

Johan A. K. Suykens, Joos Vandewalle, Samuel Xavier de Souza, 2006, Int. J. Circuit Theory Appl..

Two-level ℓ1 minimization for compressed sensing

Sabine Van Huffel, Johan A. K. Suykens, Lei Shi, 2015, Signal Process..

Robust Gradient Learning With Applications

Johan A. K. Suykens, Yunlong Feng, Yuning Yang, 2016, IEEE Transactions on Neural Networks and Learning Systems.

Bankruptcy prediction with least squares support vector machine classifiers

Johan A. K. Suykens, Bart De Moor, Bart Baesens, 2003, 2003 IEEE International Conference on Computational Intelligence for Financial Engineering, 2003. Proceedings..

Mutual synchronization of Time Delay Lur'e systems

Johan A. K. Suykens, Joos Vandewalle, Mustak E. Yalcin, 2000 .

Hybrid algorithms with applications to sparse and low rank regularization

Johan A. K. Suykens, Marco Signoretto, Andreas Argyriou, 2014 .

Links between NLq neural control theory and robust control theory

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 1996 .

Soft kernel spectral clustering

Johan A. K. Suykens, Rocco Langone, Raghvendra Mall, 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

Application of Kernel Principal Component Analysis for Single-Lead-ECG-Derived Respiration

Sabine Van Huffel, Johan A. K. Suykens, Carolina Varon, 2012, IEEE Transactions on Biomedical Engineering.

Optimal reduced sets for sparse kernel spectral clustering

Johan A. K. Suykens, Rocco Langone, Raghvendra Mall, 2014, 2014 International Joint Conference on Neural Networks (IJCNN).

Learning solutions to partial differential equations using LS-SVM

Johan A. K. Suykens, Siamak Mehrkanoon, J. Suykens, 2015, Neurocomputing.

Fixed-size Pegasos for hinge and pinball loss SVM

Johan A. K. Suykens, Xiaolin Huang, Vilen Jumutc, 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

Modified Frank-Wolfe Algorithm for Enhanced Sparsity in Support Vector Machine Classifiers

Johan A. K. Suykens, Carlos M. Alaíz, 2018, Neurocomputing.

Spatio-temporal feature selection for black-box weather forecasting

Johan A. K. Suykens, Zahra Karevan, 2016, ESANN.

Cellular Neural Networks, Multi-Scroll Chaos and Synchronization

Johan A. K. Suykens, Joos Vandewalle, Mustak E. Yalcin, 2005 .

Nonlinear System Modeling

Johan A. K. Suykens, Joos Vandewalle, Shaohua Tan, 1998 .

Genetic Weight Optimization of a Feedforward Neural Network Controller

Johan A. K. Suykens, Joos Vandewalle, Dirk Thierens, 1993 .

Improved generalization ability of neurocontrollers by imposing NLq stability constraints

Johan A. K. Suykens, Joos Vandewalle, J. Suykens, 1998, ESANN.

Efficient ensemble learning with support vector machines

Johan A. K. Suykens, Bart De Moor, Marc Claesen, 2014 .

Approximate Confidence and Prediction Intervals for Least Squares Support Vector Regression

Johan A. K. Suykens, Bart De Moor, Kris De Brabanter, 2011, IEEE Transactions on Neural Networks.

Hybrid Conditional Gradient - Smoothing Algorithms with Applications to Sparse and Low Rank Regularization pdf

Johan A. K. Suykens, Marco Signoretto, Andreas Argyriou, 2014, ArXiv.

Modelling the strip thickness in hot steel rolling mills using least‐squares support vector machines

Johan A. K. Suykens, Kaixiang Peng, Steven X. Ding, 2018 .

Learning Transformation Models for Ranking and Survival Analysis

Sabine Van Huffel, Vanya Van Belle, Johan A. K. Suykens, 2011, J. Mach. Learn. Res..

NLq Theory: A Neural Control Framework with Global Asymptotic Stability Criteria

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 1997, Neural Networks.

QoS prediction for web service compositions using kernel-based quantile estimation with online adaptation of the constant offset

Johan A. K. Suykens, Joos Vandewalle, Wouter Joosen, 2014, Inf. Sci..

Comparing Methods for Multi-class Probabilities in Medical Decision Making Using LS-SVMs and Kernel Logistic Regression

Sabine Van Huffel, Johan A. K. Suykens, Dirk Timmerman, 2007, ICANN.

Volatility Tube Support Vector Machines

Johan A. K. Suykens, Bart De Moor, Tony Van Gestel, 2000 .

Multilevel Hierarchical Kernel Spectral Clustering for Real-Life Large Scale Complex Networks

Johan A. K. Suykens, Rocco Langone, Raghvendra Mall, 2014, PloS one.

Weighted Coordinate-Wise Pegasos

Johan A. K. Suykens, Vilen Jumutc, J. Suykens, 2013, PReMI.

Clustering-based feature selection for black-box weather temperature prediction

Johan A. K. Suykens, Zahra Karevan, 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

Robust artefact detection in long-term ECG recordings based on autocorrelation function similarity and percentile analysis

Sabine Van Huffel, Johan A. K. Suykens, Carolina Varon, 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

Complex networks, synchronization and cooperative behaviour

Johan A. K. Suykens, J. Suykens, 2006 .

Incorporating structural information from the multichannel EEG improves patient-specific seizure detection

Wim Van Paesschen, Johan A. K. Suykens, Maarten De Vos, 2012, Clinical Neurophysiology.

Sparse LS-SVMs using additive regularization with a penalized validation criterion

Johan A. K. Suykens, Bart De Moor, Kristiaan Pelckmans, 2004, ESANN.

Improved Long-Term Temperature Prediction by Chaining of Neural Networks

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 2001, Int. J. Neural Syst..

Concurrent monitoring of operating condition deviations and process dynamics anomalies with slow feature analysis

Johan A. K. Suykens, Dexian Huang, Chao Shang, 2015 .

Estimating the unknown time delay in chemical processes

Johan A. K. Suykens, Steven X. Ding, Siamak Mehrkanoon, 2016, Eng. Appl. Artif. Intell..

A two-experiment approach to Wiener system identification

Johan A. K. Suykens, Giulio Bottegal, Ricardo Castro-Garcia, 2018, Autom..

M@CBETH: optimizing clinical microarray classification

Kathleen Marchal, Johan A. K. Suykens, Bart De Moor, 2005, 2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05).

Clustering data over time using kernel spectral clustering with memory

Johan A. K. Suykens, Rocco Langone, Raghvendra Mall, 2014, 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM).

Tensor-based graph analysis with applications in graph clustering (poster)

Johan A. K. Suykens, Lieven De Lathauwer, Paul Smyth, 2015 .

The use of LS-SVM in the classification of brain tumors based on 1H-MR spectroscopy signals

Sabine Van Huffel, Johan A. K. Suykens, Carles Arús, 2002 .

An initial approach to wrapped input selection using least squares support vector machine classifiers: some empirical results

Johan A. K. Suykens, Bart Baesens, Jan Vanthienen, 2000 .

Experimental confirmation of a nonlinear Hinfty synchronization scheme with 5-scroll attractors

Johan A. K. Suykens, Joos Vandewalle, Mustak E. Yalcin, 1999 .

Primal-Dual Monotone Kernel Regression

Johan A. K. Suykens, Bart De Moor, Kristiaan Pelckmans, 2005, Neural Processing Letters.

Knowledge discovery in a direct marketing case using least squares support vector machines

Johan A. K. Suykens, Bart De Moor, Bart Baesens, 2001, Int. J. Intell. Syst..

Sequential minimal optimization for SVM with pinball loss

Johan A. K. Suykens, Lei Shi, Xiaolin Huang, 2015, Neurocomputing.

On the identification of a chaotic system by means of recurrent neural state space models

Johan A. K. Suykens, Joos Vandewalle, 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

Knowledge Discovery Using Least Squares Support Vector Machine Classifiers: A Direct Marketing Case

Johan A. K. Suykens, Bart De Moor, Bart Baesens, 2000, PKDD.

NLq theory: checking and imposing stability of recurrent neural networks for nonlinear modeling

Johan A. K. Suykens, Joos Vandewalle, Bart L. R. De Moor, 1997, IEEE Trans. Signal Process..

Improved long-term temperature prediction for a blast furnace using neural networks

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 2000 .

NARX identification of hammerstein models using least squares support vector machines

Johan A. K. Suykens, Bart De Moor, Kristiaan Pelckmans, 2004 .

Multiclass Semisupervised Learning Based Upon Kernel Spectral Clustering

Johan A. K. Suykens, Rocco Langone, Raghvendra Mall, 2015, IEEE Transactions on Neural Networks and Learning Systems.

Identification of positive real models in subspace identification by using regularization

Johan A. K. Suykens, Bart De Moor, Paul Van Dooren, 2003, IEEE Trans. Autom. Control..

Towards the detection of error-related potentials and its integration in the context of a P300 speller brain-computer interface

Arne Robben, Johan A. K. Suykens, Marc M. Van Hulle, 2012, Neurocomputing.

Nonparametric comparison of signals based on statistiscal bootstrap techniques

Johan A. K. Suykens, Bart De Moor, Joseph De Brabanter, 2006 .

A Rank-One Tensor Updating Algorithm for Tensor Completion

Johan A. K. Suykens, Yunlong Feng, Yuning Yang, 2015, IEEE Signal Processing Letters.

Disentangled Representation Learning and Generation with Manifold Optimization pdf

Johan A. K. Suykens, Joachim Schreurs, Michael Fanuel, 2020, ArXiv.

Artificial Neural Networks for Modeling and Control of Non-Linear Systems

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 1995 .

Least Squares Support Vector Machine Classifiers

Johan A. K. Suykens, Joos Vandewalle, J. Suykens, 1999, Neural Processing Letters.

Neural network models as linear systems with bounded uncertainty, applicable to robust controller design

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 1993 .

Study of rupture of membranes before 26 weeks of gestation

Sabine Van Huffel, Johan A. K. Suykens, Dirk Timmerman, 2002 .

Structured Kernel Based Modeling: An Exploration in Short-Term Load Forecasting

Johan A. K. Suykens, Bart De Moor, Marcelo Espinoza, 2006 .

WINNING ENTRY OF THE K. U. LEUVEN TIME-SERIES PREDICTION COMPETITION

Johan A. K. Suykens, Joos Vandewalle, James McNames, 1999 .

Deep Restricted Kernel Machines Using Conjugate Feature Duality

Johan A. K. Suykens, J. Suykens, 2017, Neural Computation.

Alarm prediction in industrial machines using autoregressive LS-SVM models

Johan A. K. Suykens, Abdellatif Bey-Temsamani, Rocco Langone, 2014, 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM).

LS-SVM for Recurrent Networks and Control

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 2002 .

Scalable Semi-supervised kernel spectral learning using random Fourier features

Johan A. K. Suykens, Siamak Mehrkanoon, J. Suykens, 2016, 2016 IEEE Symposium Series on Computational Intelligence (SSCI).

A process model to develop an internal rating system: Sovereign credit ratings

Johan A. K. Suykens, Bart Baesens, Jan Vanthienen, 2006, Decis. Support Syst..

Multi-view LS-SVM regression for black-box temperature prediction in weather forecasting

Johan A. K. Suykens, Zahra Karevan, Lynn Houthuys, 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

A semi-supervised formulation to binary kernel spectral clustering

Johan A. K. Suykens, Carlos Alzate, J. Suykens, 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

Kernel-Based Learning from Infinite Dimensional 2-Way Tensors

Johan A. K. Suykens, Lieven De Lathauwer, Marco Signoretto, 2010, ICANN.

Support vector machines and kernel methods: new approaches in unsupervised learning

Johan A. K. Suykens, Carlos Alzate, J. Suykens, 2010 .

Wasserstein Exponential Kernels

Johan A. K. Suykens, Michael Fanuel, Henri De Plaen, 2020, 2020 International Joint Conference on Neural Networks (IJCNN).

LS-SVM approximate solution to linear time varying descriptor systems

Johan A. K. Suykens, Siamak Mehrkanoon, J. Suykens, 2012, Autom..

Multi-View Kernel Spectral Clustering

Johan A. K. Suykens, Rocco Langone, Lynn Houthuys, 2018, Inf. Fusion.

Regularized Semipaired Kernel CCA for Domain Adaptation

Johan A. K. Suykens, Siamak Mehrkanoon, J. Suykens, 2018, IEEE Transactions on Neural Networks and Learning Systems.

A flexible alarm prediction system for smart manufacturing scenarios following a forecaster–analyzer approach

Johan A. K. Suykens, Arantza Illarramendi, Kevin Villalobos, 2020, Journal of Intelligent Manufacturing.

Parameter estimation of delay differential equations: An integration-free LS-SVM approach

Johan A. K. Suykens, Siamak Mehrkanoon, Saeid Mehrkanoon, 2014, Commun. Nonlinear Sci. Numer. Simul..

Announcement: Focus issue on “Synchronization in complex networks”

Johan A. K. Suykens, Grigory V. Osipov, 2007 .

Large-Scale Clustering Algorithms

Johan A. K. Suykens, Rocco Langone, Vilen Vilen Jumutc, 2017 .

Convex Formulation for Kernel PCA and Its Use in Semisupervised Learning

Johan A. K. Suykens, Carlos M. Alaíz, Michaël Fanuel, 2016, IEEE Transactions on Neural Networks and Learning Systems.

Kernel Spectral Clustering for Big Data Networks

Johan A. K. Suykens, Rocco Langone, Raghvendra Mall, 2013, Entropy.

A combined MRI and MRSI based multiclass system for brain tumour recognition using LS-SVMs with class probabilities and feature selection

Sabine Van Huffel, Johan A. K. Suykens, Jan Luts, 2007, Artif. Intell. Medicine.

Kernel PLS variants for regression

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 2003, ESANN.

A primal-dual line search method and applications in image processing

Johan A. K. Suykens, Pantelis Sopasakis, Panagiotis Patrinos, 2017, 2017 25th European Signal Processing Conference (EUSIPCO).

Benchmarking Least Squares Support Vector Machine Classifiers

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 2004, Machine Learning.

Tensor-based restricted kernel machines for multi-view classification

Johan A. K. Suykens, Lynn Houthuys, J. Suykens, 2021, Inf. Fusion.

Multi-Scroll Chaotic and Hyperchaotic Attractors

Johan A. K. Suykens, Joos Vandewalle, Mustak E. Yalcin, 2005 .

Faculteit Economie En Bedrijfskunde Hoveniersberg 24 B-9000 Gent Bayesian Kernel-based Classification for Financial Distress Detection Dirk Van Den Poel 4 Bayesian Kernel Based Classification for Financial Distress Detection

Johan A. K. Suykens, Bart Baesens, Dirk Van den Poel, 2022 .

Multi-View Least Squares Support Vector Machines Classification

Johan A. K. Suykens, Rocco Langone, Lynn Houthuys, 2017, Neurocomputing.

Modelling the Belgian gas consumption using neural networks

Johan A. K. Suykens, Bart De Moor, Wouter Favoreel, 1996, Neural Processing Letters.

Out-of-sample eigenvectors in kernel spectral clustering

Johan A. K. Suykens, Carlos Alzate, J. Suykens, 2011, The 2011 International Joint Conference on Neural Networks.

Kernel Regression in the Presence of Correlated Errors

Johan A. K. Suykens, Bart De Moor, Kris De Brabanter, 2011, J. Mach. Learn. Res..

LSSVM based initialization approach for parameter estimation of dynamical systems

Johan A. K. Suykens, Moritz Diehl, Siamak Mehrkanoon, 2014 .

Control of a recurrent neural network emulator for the double scroll

Johan A. K. Suykens, Joos Vandewalle, J. Suykens, 1996 .

Impulse response constrained LS-SVM modelling for MIMO Hammerstein system identification

Johan A. K. Suykens, Oscar Mauricio Agudelo, Ricardo Castro-Garcia, 2019, Int. J. Control.

Improved performance on high-dimensional survival data by application of Survival-SVM

Sabine Van Huffel, Vanya Van Belle, Johan A. K. Suykens, 2011, Bioinform..

Wiener-Hammerstein Benchmark

Johan A. K. Suykens, Lennart Ljung, Johan Schoukens, 2009 .

Robust nonlinear H$_{\infty}$ synchronization of chaotic Lur'e systems

Leon O. Chua, Johan A. K. Suykens, Joos Vandewalle, 1997 .

3D-Grid Scroll Attractors

Johan A. K. Suykens, Joos Vandewalle, Mustak E. Yalcin, 2001 .

Support vector machines for survival analysis

Sabine Van Huffel, Vanya Van Belle, Johan A. K. Suykens, 2007 .

Robust Support Vector Machines for Classification with Nonconvex and Smooth Losses

Johan A. K. Suykens, Xiaolin Huang, Yunlong Feng, 2016, Neural Computation.

Pinball Loss Minimization for One-bit Compressive Sensing

Johan A. K. Suykens, Ming Yan, Lei Shi, 2015, Neurocomputing.

Stabilizing neural controllers: a case study for swinging up a double inverted pendulum

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 1993 .

On the use of a clinical kernel in survival analysis

Sabine Van Huffel, Vanya Van Belle, Johan A. K. Suykens, 2010, ESANN.

Electric Load Forecasting: Using Kernel-Based Modeling for Nonlinear System Identification

Johan A. K. Suykens, Ronnie Belmans, Bart De Moor, 2007 .

Efficient evolutionary spectral clustering

Johan A. K. Suykens, Rocco Langone, Marc Van Barel, 2016, Pattern Recognit. Lett..

Brain tumour classification using short echo time 1H MRS. Objective comparison of classification techniques (LDA, LS-SVM)

Sabine Van Huffel, Johan A. K. Suykens, Carles Arús, 2004 .

Unpaired multi-view kernel spectral clustering

Johan A. K. Suykens, Lynn Houthuys, J. Suykens, 2017, 2017 IEEE Symposium Series on Computational Intelligence (SSCI).

Support Vector Machines and Kernel Based Learning

Johan A. K. Suykens, J. Suykens, 2005 .

Transductively Learning from Positive Examples Only

Johan A. K. Suykens, Kristiaan Pelckmans, J. Suykens, 2009, ESANN.

Low rank updated LS-SVM classifiers for fast variable selection

Johan A. K. Suykens, Bart De Moor, Fabian Ojeda, 2008, Neural Networks.

A Comparison of Pruning Algorithms for Sparse Least Squares Support Vector Machines

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 2004, ICONIP.

Min-max feedback MPC using a time-varying terminal constraint set and comments on "Efficient robust constrained model predictive control with a time-varying terminal constraint set"

Johan A. K. Suykens, Bart De Moor, Bert Pluymers, 2005, Syst. Control. Lett..

A regularized kernel CCA contrast function for ICA

Johan A. K. Suykens, Carlos Alzate, J. Suykens, 2008, Neural Networks.

Tensor Learning in Multi-view Kernel PCA

Johan A. K. Suykens, Lynn Houthuys, J. Suykens, 2018, ICANN.

Bayesian Framework for Least-Squares Support Vector Machine Classifiers, Gaussian Processes, and Kernel Fisher Discriminant Analysis

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 2002, Neural Computation.

NLq theory: unifications in the theory of neural networks, systems and control

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 1995, ESANN.

Multi-scroll and hypercube attractors from Josephson junctions

Johan A. K. Suykens, Joos Vandewalle, Müstak E. Yalçin, 2006, 2006 IEEE International Symposium on Circuits and Systems.

Convex optimization for the design of learning machines

Johan A. K. Suykens, Bart De Moor, Kristiaan Pelckmans, 2007, ESANN.

Joint Regression and Linear Combination of Time Series for Optimal Prediction

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 2012, ESANN.

Robust triple mode MPC

Johan A. K. Suykens, Lars Imsland, Bert Pluymers, 2006 .

Approximate Solutions to Ordinary Differential Equations Using Least Squares Support Vector Machines

Johan A. K. Suykens, Tillmann Falck, Siamak Mehrkanoon, 2012, IEEE Transactions on Neural Networks and Learning Systems.

MINLIP: Efficient Learning of Transformation Models

Sabine Van Huffel, Vanya Van Belle, Johan A. K. Suykens, 2009, ICANN.

Hammerstein system identification through best linear approximation inversion and regularisation

Johan A. K. Suykens, Koen Tiels, Oscar Mauricio Agudelo, 2018, Int. J. Control.

Deep hybrid neural-kernel networks using random Fourier features

Johan A. K. Suykens, Siamak Mehrkanoon, J. Suykens, 2018, Neurocomputing.

Model Structure Determination and Identification with Kernel Based Partially Linear Models

Johan A. K. Suykens, Bart De Moor, Marcelo Espinoza, 2004 .

Smoothing Techniques for Distributed Model Predictive Algorithms in Networks

Johan A. K. Suykens, Ion Necoara, Ioan Dumitrache, 2009 .

A Weighted Kernel PCA Formulation with Out-of-Sample Extensions for Spectral Clustering Methods

Johan A. K. Suykens, Carlos Alzate, J. Suykens, 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

Least Squares Support Vector Machines

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 2002 .

Ranking Overlap and Outlier Points in Data using Soft Kernel Spectral Clustering

Johan A. K. Suykens, Rocco Langone, Raghvendra Mall, 2015, ESANN.

Kernel based partially linear models and nonlinear identification

Johan A. K. Suykens, Bart De Moor, Marcelo Espinoza, 2005, IEEE Transactions on Automatic Control.

Signal recovery for jointly sparse vectors with different sensing matrices

Johan A. K. Suykens, Xiaolin Huang, Li Li, 2015, Signal Process..

Asymmetric least squares support vector machine classifiers

Johan A. K. Suykens, Lei Shi, Xiaolin Huang, 2014, Comput. Stat. Data Anal..

Learning with continuous piecewise linear decision trees

Johan A. K. Suykens, Shuning Wang, Jun Xu, 2020, Expert Syst. Appl..

Support vector machines with piecewise linear feature mapping

Johan A. K. Suykens, Xiaolin Huang, Siamak Mehrkanoon, 2013, Neurocomputing.

Identification of the Silverbox Benchmark Using Nonlinear State-Space Models

Johan A. K. Suykens, Johan Schoukens, Jonas Sjöberg, 2012 .

Real-time tracking algorithm with locking on a given object for VLSI CNN-UM implementations

Johan A. K. Suykens, Joos Vandewalle, Samuel Xavier De Souza, 2004 .

Bayesian interpretation of least squares support vector machines for financial time series prediction

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 2001 .

Interpolation Based MPC with Exact Constraint Handling : the Uncertain Case

Johan A. K. Suykens, Bart De Moor, Bert Pluymers, 2005, CDC 2005.

Correntropy Based Matrix Completion

Johan A. K. Suykens, Yunlong Feng, Yuning Yang, 2018, Entropy.

The differogram: Non-parametric noise variance estimation and its use for model selection

Johan A. K. Suykens, Bart De Moor, Kristiaan Pelckmans, 2005, Neurocomputing.

Model Predictive Control : Convex Optimization Approaches Versus Constrained Dynamic Backpropagation

Johan A. K. Suykens, Bart De Moor, J. Buijs, 2001 .

Statistical machine learning approach to multimodal magnetic resonance data processing for clinical decision support in brain tumor diagnostics

Sabine Van Huffel, Johan A. K. Suykens, Dirk Vandermeulen, 2010 .

Explainable deep convolutional learning for intuitive model development by non–machine learning domain experts

Johan A. K. Suykens, Sundaravelpandian Singaravel, Philipp Geyer, 2020, Design Science.

Gedrag in complexe netwerken

Johan A. K. Suykens, 2007 .

A Generic Class of Chaotic and Hyperchaotic Circuits with Synchronization Methods

Johan A. K. Suykens, Joos Vandewalle, Mustak E. Yalcin, 2002 .

Improved Initialization for Nonlinear State-Space Modeling

Johan A. K. Suykens, Johan Schoukens, Jonas Sjöberg, 2014, IEEE Transactions on Instrumentation and Measurement.

Variogram based noise variance estimation and its use in kernel based regression

Johan A. K. Suykens, Bart De Moor, Kristiaan Pelckmans, 2003, 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718).

Variable selection and grouping with multiple graph priors

Johan A. K. Suykens, Marco Signoretto, Carlo Savorgnan, 2009, NIPS 2009.

True random bit generation from a double-scroll attractor

Johan A. K. Suykens, Joos Vandewalle, Müstak E. Yalçin, 2004, IEEE Transactions on Circuits and Systems I: Regular Papers.

Fast Adaptive Hinging Hyperplanes

Johan A. K. Suykens, Jun Xu, Shuning Wang, 2018, 2018 IEEE Conference on Decision and Control (CDC).

Discrete Time Interconnected Cellular Neural Networks Within NLq Theory

Johan A. K. Suykens, Joos Vandewalle, J. Suykens, 1996, Int. J. Circuit Theory Appl..

Automatic chip-spcific CNN template optimization using adaptive simulated Annealing

Johan A. K. Suykens, Joos Vandewalle, Samuel Xavier De Souza, 2003 .

Clustering and Staircases

Johan A. K. Suykens, Bart De Moor, Kristiaan Pelckmans, 2005, NIPS 2005.

Spatiotemporal pattern formation in the ACE16k CNN chip

Johan A. K. Suykens, Müstak E. Yalçin, 2005, 2005 IEEE International Symposium on Circuits and Systems.

Behavioral modeling of analog circuits using neural networks

Johan A. K. Suykens, Georges Gielen, Philippe Lemmerling, 1997 .

Entropy-Based Incomplete Cholesky Decomposition for a Scalable Spectral Clustering Algorithm: Computational Studies and Sensitivity Analysis

Johan A. K. Suykens, Rocco Langone, Marc Van Barel, 2016, Entropy.

Kernel spectral clustering for community detection in complex networks

Johan A. K. Suykens, Rocco Langone, Carlos Alzate, 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

Agglomerative hierarchical kernel spectral data clustering

Johan A. K. Suykens, Rocco Langone, Raghvendra Mall, 2014, 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM).

Continuous time NLq theory: absolute stability criteria

Johan A. K. Suykens, Joos Vandewalle, J. Suykens, 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

Sparse kernel spectral clustering models for large-scale data analysis

Johan A. K. Suykens, Carlos Alzate, 2011, Neurocomputing.

Efficient Sparse Approximation of Support Vector Machines Solving a Kernel Lasso

Johan A. K. Suykens, Ricardo Ñanculef, Emanuele Frandi, 2016, CIARP.

Interval coded scoring systems for survival analysis

Sabine Van Huffel, Vanya Van Belle, Johan A. K. Suykens, 2012, ESANN.

Kernel Methods

Johan A. K. Suykens, Marco Signoretto, 2015, Handbook of Computational Intelligence.

Representative subsets for big data learning using k-NN graphs

Johan A. K. Suykens, Rocco Langone, Raghvendra Mall, 2014, 2014 IEEE International Conference on Big Data (Big Data).

The K.U.Leuven competition data: a challenge for advanced neural network techniques

Johan A. K. Suykens, Joos Vandewalle, 2000, ESANN.

Hyperchaotic n-scroll attractors

Johan A. K. Suykens, Joos Vandewalle, Mustak E. Yalcin, 2000 .

Kernel Component Analysis Using an Epsilon-Insensitive Robust Loss Function

Johan A. K. Suykens, Carlos Alzate, J. Suykens, 2008, IEEE Transactions on Neural Networks.

Denoised Kernel Spectral data Clustering

Johan A. K. Suykens, Rocco Langone, Raghvendra Mall, 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

On the use of Bayesian Learning Neural Networks for TCAD Empirical modeling

Johan A. K. Suykens, Joos Vandewalle, M. D. Profirescu, 2002 .

Magnetic eigenmaps for community detection in directed networks

Johan A. K. Suykens, Carlos M. Alaíz, Michaël Fanuel, 2016, Physical review. E.

Semi-supervised Learning of Sparse Linear Models in Mass Spectral Imaging

Johan A. K. Suykens, Bart De Moor, Marco Signoretto, 2010, PRIB.

Variable selection by rank-one updates for least squares support vector machines

Johan A. K. Suykens, Bart De Moor, Fabian Ojeda, 2007, 2007 International Joint Conference on Neural Networks.

Learning from partially labeled data

Johan A. K. Suykens, Xiaolin Huang, Siamak Mehrkanoon, 2020, ESANN.

Multiway Spectral Clustering with Out-of-Sample Extensions through Weighted Kernel PCA

Johan A. K. Suykens, Carlos Alzate, J. Suykens, 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Modularity-based model selection for kernel spectral clustering

Johan A. K. Suykens, Rocco Langone, Carlos Alzate, 2011, The 2011 International Joint Conference on Neural Networks.

Robustness analysis for Least Squares kernel based regression: an optimization approach

Johan A. K. Suykens, Bart De Moor, Tillmann Falck, 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

Highly Sparse Reductions to Kernel Spectral Clustering

Johan A. K. Suykens, Rocco Langone, Raghvendra Mall, 2013, PReMI.

Weighted Multi-view Deep Neural Networks for Weather Forecasting

Johan A. K. Suykens, Zahra Karevan, Lynn Houthuys, 2018, ICANN.

Support and Quantile Tubes pdf

Johan A. K. Suykens, Bart De Moor, Kristiaan Pelckmans, 2007, ArXiv.

Pattern classification with CNNs as reservoirs

Johan A. K. Suykens, Benjamin Schrauwen, Dirk Stroobandt, 2008 .

M@cbeth: a Microarray Classification Benchmarking Tool

Kathleen Marchal, Johan A. K. Suykens, Bart De Moor, 2005 .

Reducing the Number of Support Vectors of SVM Classifiers Using the Smoothed Separable Case Approximation

Johan A. K. Suykens, Joos Vandewalle, Dries Geebelen, 2012, IEEE Transactions on Neural Networks and Learning Systems.

Multi-class kernel logistic regression: a fixed-size implementation

Johan A. K. Suykens, Peter Karsmakers, Kristiaan Pelckmans, 2007, IJCNN.

Robustness of Kernel Based Regression: A Comparison of Iterative Weighting Schemes

Johan A. K. Suykens, Bart De Moor, Kristiaan Pelckmans, 2009, ICANN.

Improved non-parametric sparse recovery with data matched penalties

Johan A. K. Suykens, Lieven De Lathauwer, Kristiaan Pelckmans, 2010, 2010 2nd International Workshop on Cognitive Information Processing.

Signal processing theory and machine learning

Sergios Theodoridis, Rama Chellappa, Johan A. K. Suykens, 2014 .

Static and dynamic stabilizing neural controllers, applicable to transition between equilibrium points

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 1994, Neural Networks.

Preoperative prediction of malignancy of ovarian tumors using least squares support vector machines

Sabine Van Huffel, Johan A. K. Suykens, Dirk Timmerman, 2003, Artif. Intell. Medicine.

Hierarchical kernel spectral clustering

Johan A. K. Suykens, Carlos Alzate, J. Suykens, 2012, Neural Networks.

Advances in learning theory : methods, models and applications

Johan A. K. Suykens, J. Suykens, 2003 .

Kernel Spectral Clustering and applications pdf

Johan A. K. Suykens, Rocco Langone, Raghvendra Mall, 2015, ArXiv.

Large-Scale Kernel Logistic Regression for Segment-Based Phoneme Recognition

Johan A. K. Suykens, Peter Karsmakers, Kristiaan Pelckmans, 2009 .

Axiomatic Kernels on Graphs for Support Vector Machines

Johan A. K. Suykens, Marcin Orchel, 2019, ICANN.

Non-linear circuit theory : from private playground for academic researchers to innovative information processing circuits

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 1998 .

Robust local stability of multilayer recurrent neural networks

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 2000, IEEE Trans. Neural Networks Learn. Syst..

Bagging Linear Sparse Bayesian Learning Models for Variable Selection in Cancer Diagnosis

Sabine Van Huffel, Johan A. K. Suykens, Chuan Lu, 2007, IEEE Transactions on Information Technology in Biomedicine.

A robust ensemble approach to learn from positive and unlabeled data using SVM base models pdf

Johan A. K. Suykens, Bart De Moor, Marc Claesen, 2014, Neurocomputing.

Benchmarking state-of-the-art classification algorithms for credit scoring

Johan A. K. Suykens, Bart Baesens, Jan Vanthienen, 2003, J. Oper. Res. Soc..

A comparison between various empirical models for TCAD purposes

Johan A. K. Suykens, Joos Vandewalle, M. D. Profirescu, 2000, 2000 International Semiconductor Conference. 23rd Edition. CAS 2000 Proceedings (Cat. No.00TH8486).

Large scale semi-supervised learning using KSC based model

Johan A. K. Suykens, Siamak Mehrkanoon, J. Suykens, 2014, 2014 International Joint Conference on Neural Networks (IJCNN).

Linear and Nonlinear Preoperative Classification of Ovarian Tumors

Sabine Van Huffel, Johan A. K. Suykens, Dirk Timmerman, 2004 .

A kernel-based framework to tensorial data analysis

Johan A. K. Suykens, Lieven De Lathauwer, Marco Signoretto, 2011, Neural Networks.

Robust classification of graph-based data

Johan A. K. Suykens, Carlos M. Alaíz, Michaël Fanuel, 2016, Data Mining and Knowledge Discovery.

Quadratically Constrained Quadratic Programming for Subspace Selection in Kernel Regression Estimation

Johan A. K. Suykens, Kristiaan Pelckmans, Marco Signoretto, 2008, ICANN.

Indefinite kernel spectral learning

Johan A. K. Suykens, Xiaolin Huang, Siamak Mehrkanoon, 2018, Pattern Recognit..

A dual interior-point distributed algorithm for large-scale data networks optimization

Johan A. K. Suykens, Ion Necoara, 2009, 2009 European Control Conference (ECC).

Automatic Seizure Detection Incorporating Structural Information

Wim Van Paesschen, Sabine Van Huffel, Johan A. K. Suykens, 2011, ICANN.

Robust triple mode MPC

Johan A. K. Suykens, Lars Imsland, Bert Pluymers, 2006, 2006 American Control Conference.

Kernel Spectral Clustering and

Johan A. K. Suykens, Rocco Langone, Raghvendra Mall, 2015 .

A proximal center-based decomposition method for multi-agent convex optimization

Johan A. K. Suykens, Ion Necoara, 2008, 2008 47th IEEE Conference on Decision and Control.

Nonlinear system identification using neural networks

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 1996 .

Optimal Data Projection for Kernel Spectral Clustering

Johan A. K. Suykens, Germán Castellanos-Domínguez, Diego Hernán Peluffo-Ordóñez, 2014, ESANN.

Lur'e systems with multilayer perceptron and recurrent neural networks: absolute stability and dissipativity

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 1999, IEEE Trans. Autom. Control..

Least squares support vector machine classifiers: an empirical evaluation

Johan A. K. Suykens, Bart De Moor, Bart Baesens, 2000 .

Regularized and sparse stochastic k-means for distributed large-scale clustering

Johan A. K. Suykens, Rocco Langone, Vilen Jumutc, 2015, 2015 IEEE International Conference on Big Data (Big Data).

Robust Generative Restricted Kernel Machines using Weighted Conjugate Feature Duality

Johan A. K. Suykens, Joachim Schreurs, Arun Pandey, 2020, LOD.

Quantile regression with ℓ1—regularization and Gaussian kernels

Johan A. K. Suykens, Lei Shi, Xiaolin Huang, 2014, Adv. Comput. Math..

Noise Level Estimation for Model Selection in Kernel PCA Denoising

Johan A. K. Suykens, Carlos Alzate, Carolina Varon, 2015, IEEE Transactions on Neural Networks and Learning Systems.

Explaining Support Vector Machines: A Color Based Nomogram

Johan A. K. Suykens, Sabine Van Huffel, Ben Van Calster, 2016, PloS one.

On the relevance of automatically selected single-voxel MRS and multimodal MRI and MRSI features for brain tumour differentiation

Sabine Van Huffel, Johan A. K. Suykens, Jan Luts, 2011, Comput. Biol. Medicine.

New bilinear formulation to semi-supervised classification based on Kernel Spectral Clustering

Johan A. K. Suykens, Vilen Jumutc, J. Suykens, 2014, 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM).

Asymmetric v-tube support vector regression

Johan A. K. Suykens, Lei Shi, Xiaolin Huang, 2014, Comput. Stat. Data Anal..

Sparse kernel models for spectral clustering using the incomplete Cholesky decomposition

Johan A. K. Suykens, Carlos Alzate, J. Suykens, 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

Scalable Hybrid Deep Neural Kernel Networks

Andreas Zell, Johan A. K. Suykens, Siamak Mehrkanoon, 2017, ESANN.

Time Series Prediction Competition

Johan A. K. Suykens, Joos Vandewalle, James McNames, 1999 .

Differentiation between brain metastases and glioblastoma multiforme based on MRI, MRS and MRSI

Sabine Van Huffel, Johan A. K. Suykens, Jan Luts, 2009, 2009 22nd IEEE International Symposium on Computer-Based Medical Systems.

Transductive Rademacher Complexities for Learning Over a Graph

Johan A. K. Suykens, Kristiaan Pelckmans, 2007, MLG.

Hybrid Coupled Local Minimizers

Johan A. K. Suykens, Xuyang Lou, 2014, IEEE Transactions on Circuits and Systems I: Regular Papers.

Families of scroll Grid attractors

Johan A. K. Suykens, Joos Vandewalle, Müstak E. Yalçin, 2002, Int. J. Bifurc. Chaos.

Weighted least squares support vector machines: robustness and sparse approximation

Johan A. K. Suykens, Joos Vandewalle, Lukas Lukas, 2002, Neurocomputing.

LS-SVM based spectral clustering and regression for predicting maintenance of industrial machines

Johan A. K. Suykens, Wannes Meert, Bart De Ketelaere, 2015, Eng. Appl. Artif. Intell..

An SVD-free Approach to a Class of Structured Low Rank Matrix Optimization Problems with Application to System Identification

Volkan Cevher, Johan A. K. Suykens, Marco Signoretto, 2013, CDC 2013.

Distributed nonlinear optimal control using sequential convex programming and smoothing techniques

Johan A. K. Suykens, Dinh Quoc Tran, Moritz Diehl, 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

Cooperative Behavior in Coupled Simulated Annealing Processes with Variance Control

Johan A. K. Suykens, Joos Vandewalle, Samuel Xavier De Souza, 2006 .

State-of-the-Art and Evolution in Public Data Sets and Competitions for System Identification, Time Series Prediction and Pattern Recognition

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

Magnetic Eigenmaps for Visualization of Directed Networks pdf

Johan A. K. Suykens, Carlos M. Alaíz, Ángela Fernández, 2016, ArXiv.

Training multilayer perceptron classifiers based on a modified support vector method

Johan A. K. Suykens, Joos Vandewalle, J. Suykens, 1999, IEEE Trans. Neural Networks.

Fast kernel spectral clustering

Johan A. K. Suykens, Rocco Langone, J. Suykens, 2017, Neurocomputing.

Absolute stability criterion for a Lur'e problem with multilayer perceptron nonlinearity

Johan A. K. Suykens, Joos Vandewalle, J. Suykens, 1996 .

DynOpt: Incorporating dynamics into mean-variance portfolio optimization

Johan A. K. Suykens, Marco Signoretto, 2013, 2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr).

Multilinear spectral regularizatioin for Kernel-based multitask learning

Johan A. K. Suykens, Marco Signoretto, 2013, NIPS 2013.

A support vector machine formulation to PCA analysis and its kernel version

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 2003, IEEE Trans. Neural Networks.

Componentwise Least Squares Support Vector Machines pdf

Johan A. K. Suykens, Bart De Moor, Kristiaan Pelckmans, 2005, ArXiv.

Moving Least Squares Support Vector Machines for weather temperature prediction

Johan A. K. Suykens, Yunlong Feng, Zahra Karevan, 2017, ESANN.

Symbolic computing of LS-SVM based models

Johan A. K. Suykens, Li Jiang, Carlos Alzate, 2011, ESANN.

Chaos control using least‐squares support vector machines

Johan A. K. Suykens, Joos Vandewalle, J. Suykens, 1999, Int. J. Circuit Theory Appl..

Non-parallel semi-supervised classification based on kernel spectral clustering

Johan A. K. Suykens, Siamak Mehrkanoon, J. Suykens, 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

A Perturbation Analysis using Second Order Cone Programming for Robust Kernel Based Regression

Johan A. K. Suykens, Bart De Moor, Marcelo Espinoza, 2008 .

Kernel Density Estimation for Dynamical Systems

Johan A. K. Suykens, Yunlong Feng, Ingo Steinwart, 2016, J. Mach. Learn. Res..

Solution Path for Pin-SVM Classifiers With Positive and Negative $\tau $ Values

Johan A. K. Suykens, Lei Shi, Xiaolin Huang, 2017, IEEE Transactions on Neural Networks and Learning Systems.

Subspace identification of Hammerstein systems using least squares support vector machines

Johan A. K. Suykens, Bart De Moor, Kristiaan Pelckmans, 2005, IEEE Transactions on Automatic Control.

The use of LS-SVM in the classification of brain tumors based on Magnetic Resonance Spectroscopy signals

Sabine Van Huffel, Johan A. K. Suykens, Carles Arús, 2002, The European Symposium on Artificial Neural Networks.

Reweighted l 2-Regularized Dual Averaging Approach for Highly Sparse Stochastic Learning

Johan A. K. Suykens, Vilen Jumutc, J. Suykens, 2014, ISNN.

Survival SVM: a practical scalable algorithm

Sabine Van Huffel, Vanya Van Belle, Johan A. K. Suykens, 2008, ESANN.

Hammerstein system identification using LS-SVM and steady state time response

Johan A. K. Suykens, Koen Tiels, Oscar Mauricio Agudelo, 2016, 2016 European Control Conference (ECC).

Learning Transformation Models for Failure Time Data

Sabine Van Huffel, Vanya Van Belle, Johan A. K. Suykens, 2008 .

Multi-label semi-supervised learning using regularized kernel spectral clustering

Johan A. K. Suykens, Siamak Mehrkanoon, J. Suykens, 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

Efficiently updating and tracking the dominant kernel principal components

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 2007, Neural Networks.

Fast in-memory spectral clustering using a fixed-size approach

Johan A. K. Suykens, Rocco Langone, Raghvendra Mall, 2016, ESANN.

Editorial: A Successful Year and Looking Forward to 2017 and Beyond

Meng Wang, Haibo He, Shiliang Sun, 2017, IEEE Trans. Neural Networks Learn. Syst..

Optimized data fusion for K-means Laplacian clustering

Johan A. K. Suykens, Bart De Moor, Shi Yu, 2011, Bioinform..

Nuclear norm regularization for overparametrized Hammerstein systems

Johan A. K. Suykens, Bart De Moor, Johan Schoukens, 2010, 49th IEEE Conference on Decision and Control (CDC).

On-Line Learning Fokker-Planck Machine

Johan A. K. Suykens, Joos Vandewalle, Herman Verrelst, 1998, Neural Processing Letters.

Parallelized Tensor Train Learning of Polynomial Classifiers

Johan A. K. Suykens, Kim Batselier, Ngai Wong, 2016, IEEE Transactions on Neural Networks and Learning Systems.

Short Term Chaotic Time Series Prediction using Symmetric LS-SVM Regression

Johan A. K. Suykens, Bart De Moor, Marcelo Espinoza, 2005 .

Learning Theory Estimates with Observations from General Stationary Stochastic Processes

Johan A. K. Suykens, Yunlong Feng, Ingo Steinwart, 2016, Neural Computation.

Sparse conjugate directions pursuit with application to fixed-size kernel models

Johan A. K. Suykens, Hugo Van hamme, Peter Karsmakers, 2011, Machine Learning.

Multi-Class Supervised Novelty Detection

Johan A. K. Suykens, Vilen Jumutc, J. Suykens, 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

A double scroll based true random bit generator

Johan A. K. Suykens, Joos Vandewalle, Müstak E. Yalçin, 2004, 2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512).

Sleep apnea classification using least-squares support vector machines on single lead ECG

Sabine Van Huffel, Johan A. K. Suykens, Carolina Varon, 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

Experimental confirmation of nonlinear Hinfty synchronization for Chua's circuit

Johan A. K. Suykens, Joos Vandewalle, Mustak E. Yalcin, 1999 .

A Convex Approach to Validation-Based Learning of the Regularization Constant

Johan A. K. Suykens, Bart De Moor, Kristiaan Pelckmans, 2007, IEEE Transactions on Neural Networks.

Fixed-Size LS-SVM Applied to the Wiener-Hammerstein Benchmark

Johan A. K. Suykens, Peter Karsmakers, Kristiaan Pelckmans, 2009 .

Compactly Supported RBF Kernels for Sparsifying the Gram Matrix in LS-SVM Regression Models

Johan A. K. Suykens, Bart De Moor, Bart Hamers, 2002, ICANN.

High level high performance computing for multitask learning of time-varying models

Johan A. K. Suykens, Marco Signoretto, Emanuele Frandi, 2014, 2014 IEEE Symposium on Computational Intelligence in Big Data (CIBD).

Robust matrix completion through nonconvex approaches and efficient algorithms

Johan A. K. Suykens, Yunlong Feng, Yuning Yang, 2016 .

Master-Slave Synchronization of Lur'e Systems with Time-Delay

Johan A. K. Suykens, Joos Vandewalle, Müstak E. Yalçin, 2001, Int. J. Bifurc. Chaos.

Sparse approximation using least squares support vector machines

Johan A. K. Suykens, Joos Vandewalle, Lukas Lukas, 2000, 2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353).

Topranking : predicting the most relevant element of a set

Johan A. K. Suykens, Kristiaan Pelckmans, 2008 .

Robust Nonlinear H Synchronization of Chaotic Lur'e Systems

Johan A. K. Suykens, Joos Vandewalle, Leon O. Chua, 1997 .

Nonlinear modeling : advanced black-box techniques

Johan A. K. Suykens, Joos Vandewalle, J. Suykens, 1998 .

Complexity Issues and Randomization Strategies in Frank-Wolfe Algorithms for Machine Learning pdf

Johan A. K. Suykens, Ricardo Ñanculef, Emanuele Frandi, 2014, NIPS 2014.

Additive Regularization Trade-Off: Fusion of Training and Validation Levels in Kernel Methods

Johan A. K. Suykens, Bart De Moor, Kristiaan Pelckmans, 2006, Machine Learning.

Probabilistic matrix factorization from quantized measurements

Johan A. K. Suykens, Giulio Bottegal, J. Suykens, 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

Hinging Hyperplanes for Time-Series Segmentation

Johan A. K. Suykens, Xiaolin Huang, Marin Matijas, 2013, IEEE Transactions on Neural Networks and Learning Systems.

Regularization, Optimization, Kernels, and Support Vector Machines

Johan A. K. Suykens, Marco Signoretto, Andreas Argyriou, 2014 .

Bayesian Inference for LS-SVM Models

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 2002 .

LS-SVM for Unsupervised Learning

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 2002 .

Optimal control by least squares support vector machines

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 2001, Neural Networks.

Coupled Local Minimizers: a new global optimization method

Johan A. K. Suykens, Guido De Roeck, Anne Teughels, 2003 .

Reweighted stochastic learning

Johan A. K. Suykens, Vilen Jumutc, J. Suykens, 2016, Neurocomputing.

NL$_{q}$ theory: a unifying framework for analysis, design and applications of complex nonlinear systems

Johan A. K. Suykens, Joos Vandewalle, J. Suykens, 1995 .

Non-parallel support vector classifiers with different loss functions

Johan A. K. Suykens, Xiaolin Huang, Siamak Mehrkanoon, 2014, Neurocomputing.

Least Conservative Support and Tolerance Tubes

Johan A. K. Suykens, Bart De Moor, Kristiaan Pelckmans, 2009, IEEE Transactions on Information Theory.

International workshop on advances in regularization, optimization, kernel methods and support vector machines : theory and applications (ROKS 2013)

Vanya Van Belle, Johan A. K. Suykens, Joos Vandewalle, 2013 .

Generative Restricted Kernel Machines pdf

Johan A. K. Suykens, Joachim Schreurs, Arun Pandey, 2019, Neural Networks.

Building sparse representations and structure determination on LS-SVM substrates

Johan A. K. Suykens, Bart De Moor, Kristiaan Pelckmans, 2005, Neurocomputing.

Introduction to Machine Learning

Johan A. K. Suykens, J. Suykens, 2014 .

The effect of imposing 'fractional abundance constraints' onto the multilayer perceptron for sub-pixel land cover classification

Johan A. K. Suykens, Jos Van Orshoven, Stien Heremans, 2016, Int. J. Appl. Earth Obs. Geoinformation.

Polynomial componentwise LS-SVM: Fast variable selection using low rank updates

Johan A. K. Suykens, Bart De Moor, Tillmann Falck, 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

Impulse Response Constrained LS-SVM modeling for Hammerstein System Identification

Johan A. K. Suykens, Mauricio Agudelo Manozca, Ricardo Castro, 2017 .

Optimized fixed-size kernel models for large data sets

Johan A. K. Suykens, Bart De Moor, Kris De Brabanter, 2010, Comput. Stat. Data Anal..

Classification of Multichannel Signals With Cumulant-Based Kernels

Johan A. K. Suykens, Marco Signoretto, Lieven De Lathauwer, 2012, IEEE Transactions on Signal Processing.

Confidence bands for least squares support vector machine classifiers: A regression approach

Johan A. K. Suykens, Bart De Moor, Peter Karsmakers, 2012, Pattern Recognit..

Generative Restricted Kernel Machines: A framework for multi-view generation and disentangled feature learning

Johan A. K. Suykens, Joachim Schreurs, Arun Pandey, 2021, Neural Networks.

The Bures Metric for Generative Adversarial Networks

Johan A. K. Suykens, Bart De Moor, Joachim Schreurs, 2021, ECML/PKDD.

A novel neural grey system model with Bayesian regularization and its applications

Johan A. K. Suykens, Xin Ma, Mei Xie, 2021, Neurocomputing.

Kernel Machines in Time (Invited Talk)

Johan A. K. Suykens, 2021, TIME.

On Robustness in Kernel Based Regression

Johan A. K. Suykens, Bart De Moor, Peter Karsmakers, 2010, NIPS 2010.

A MULTI PARAMETRIC QUADRATIC PROGRAMMING SOLUTION TO ROBUST PREDICTIVE CONTROL

Johan A. K. Suykens, Bert Pluymers, J. A. Rossiter, 2005 .

Nosologic imaging of the brain : combined MRI and MRSI segmentation and classification

Sabine Van Huffel, Johan A. K. Suykens, Dirk Vandermeulen, 2008 .

Kernel Spectral Clustering: Model Representations, Sparsity and Out-of-sample Extensions

Johan A. K. Suykens, Carlos Alzate, 2011 .

Nonparametric comparison of densities based on statistical bootstrap

Johan A. K. Suykens, Bart De Moor, Peter Karsmakers, 2010 .

An LMI-based constrained MPC scheme with time-varying terminal cost

Johan A. K. Suykens, Bert Pluymers, B. De Moor, 2004 .

Impulsive synchronization of chaotic Lur'e systems: state feedback case

Leon O. Chua, Johan A. K. Suykens, J. Vandewalle, 1998, Proceedings of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171).

Patient-specific seizure detection with limited training period

Wim Van Paesschen, Sabine Van Huffel, Johan A. K. Suykens, 2011 .

Consensus over Ring Networks as a Quadratic Optimal Control Problem

Johan A. K. Suykens, Dirk Aeyels, J. Suykens, 2010 .

Kernel Regression with Correlated Errors

Johan A. K. Suykens, B. De Moor, J. De Brabanter, 2010 .

Bayesian input selection for nonlinear regression with LS-SVMS

Johan A. K. Suykens, Marcelo Espinoza, B. De Moor, 2003 .

Between n-double sinks and n-double scrolls (n = 1,2,3,4,\ldots)

Johan A. K. Suykens, Joos Vandewalle, J. Suykens, 1993 .

Quasi-linear approach to nonlinear-systems and the design of n-double scroll (n = 1, 2, 3, 4, ...)

Johan A. K. Suykens, Joos Vandewalle, J. Suykens, 1991 .

Nonlinear H$_{\infty}$ synchronization of chaotic Lur'e systems

Leon O. Chua, Johan A. K. Suykens, Joos Vandewalle, 1997 .

Smoothing Techniques-Based Distributed Model Predictive Control Algorithms for Networks

Johan A. K. Suykens, Ion Necoara, Ioan Dumitrache, 2012 .

Kernel spectral clustering of time series in the CoRoT exoplanet database

Johan A. K. Suykens, Jonas Debosscher, Carlos Alzate, 2011 .

Nuclear Norm Regularization for Overparametrized

Johan A. K. Suykens, Bart De Moor, Johan Schoukens, 2010 .

Support Vector Machines : Least Squares Approaches and Extensions

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 2003 .

LS-SVM REGRESSION WITH AUTOCORRELATED ERRORS

Johan A. K. Suykens, Bart De Moor, Marcelo Espinoza, 2006 .

Fast kernel spectral clustering based on incomplete Cholesky factorization for large scale data analysis

Johan A. K. Suykens, Rocco Langone, Carlos Alzate, 2014 .

Robust synthesis of constrained linear state feedback using LMIs and polyhedral invariant sets

Johan A. K. Suykens, Bart De Moor, Mayuresh V. Kothare, 2006, 2006 American Control Conference.

Generating quantum-measurement probabilities from an optimality principle

Johan A. K. Suykens, J. Suykens, 2013 .

Improved Microarray-Based Decision Support with Graph Encoded Interactome Data

Johan A. K. Suykens, Olivier Gevaert, Marco Signoretto, 2010, PloS one.

Identifying positive real models in subspace identification by using regularization

Johan A. K. Suykens, Bart De Moor, Paul Van Dooren, 2003 .

Impulsive control of nonautonomous chaotic systems using practical stabilization

Leon O. Chua, Johan A. K. Suykens, J. Suykens, 1998 .

The Kingdom-Capacity of a Graph: On the Difficulty of Learning a Graph Labelling

Johan A. K. Suykens, Bart De Moor, Kristiaan Pelckmans, 2006 .

Support Vector Machine Classifier With Pinball Loss.

Johan A. K. Suykens, Xiaolin Huang, Lei Shi, 2014, IEEE transactions on pattern analysis and machine intelligence.

Feedback linearization of nonlinear distortion in electrodynamic loudspeakers

Johan A. K. Suykens, Joos Vandewalle, J. Suykens, 1995 .

A CNN Approach to Brian-Like Chaos-Periodicity Transitions

Leon O. Chua, Johan A. K. Suykens, Alberto P. Muñuzuri, 1998 .

Enhancing Dynamic Soft Sensors based on DPLS: a Temporal Smoothness Regularization Approach

Johan A. K. Suykens, Dexian Huang, Chao Shang, 2015 .

A convex relaxation for sparse low-rank Nyström approximation of large scale kernel-matrices

Johan A. K. Suykens, Bertrand Gauthier, 2016 .

Quasilinear approach to nonlinear systems and the design of n-double scroll (n=1, 2, 3, 4, . . .)

Johan A. K. Suykens, Joos Vandewalle, J. Suykens, 1991 .

Global Optimization with Coupled Local Minimizers Excited by GaussianWhite Noise

Johan A. K. Suykens, Joos Vandewalle, Serkan Gunel, 2006 .

Bayesian Learning and the Fokker-Planck machine

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 1998 .

A SIMPLE ALGORITHM FOR ROBUST MPC

Johan A. K. Suykens, Bert Pluymers, J. A. Rossiter, 2005 .

n-scroll chaos generators: a simple circuit model

Johan A. K. Suykens, Joos Vandewalle, Serdar Ozoguz, 2001 .

Global optimization by coupled local minimizers and its application to FE model updating

Johan A. K. Suykens, Guido De Roeck, Anne Teughels, 2003 .

Coupled local minimizers: alternative formulations and extensions

Johan A. K. Suykens, Joos Vandewalle, J. Suykens, 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

Efficiently updating and tracking the dominant Kernel Eigenspace

Johan A. K. Suykens, Joos Vandewalle, Lieven De Lathauwer, 2004 .

A Bayesian nonlinear support vector machine error correction model

Johan A. K. Suykens, Bart De Moor, Bart Baesens, 2006 .

Learning of Spatiotemporal Behavior in Cellular Neural Networks ∗

Johan A. K. Suykens, Samuel Xavier-de-Souza, J. Suykens, 2005 .

Constructionof Reduced Complexity Polyhedral Invariant Sets for LPV Systems using Linear Programming

Johan A. K. Suykens, Bert Pluymers, B. De Moor, 2005 .

Identification of Wiener-Hammerstein Systems using LS-SVMs

Johan A. K. Suykens, Bart De Moor, Kristiaan Pelckmans, 2009 .

Subspace algorithms for system identification and stochastic realization

Johan A. K. Suykens, Bart De Moor, P. Van Overschee, 1991 .