Amaury Lendasse

发表

Gaussian Fitting Based FDA for Chemometrics

Tuomas Kärnä, Amaury Lendasse, 2007, IWANN.

Variable Selection for Financial Modeling

Amaury Lendasse, Qi Yu, Eric Séverin, 2007 .

Local receptive fields based extreme learning machine with hybrid filter kernels for image classification

Yan Song, Amaury Lendasse, Bo He, 2018, Multidimens. Syst. Signal Process..

ELMVIS+: Fast nonlinear visualization technique based on cosine distance and extreme learning machines

Amaury Lendasse, Yoan Miche, Kaj-Mikael Björk, 2016, Neurocomputing.

Mutual Information Based Initialization of Forward-Backward Search for Feature Selection in Regression Problems

Ginés Rubio, Ignacio Rojas, Alberto Guillén, 2009, ICANN.

Determination of the Mahalanobis matrix using nonparametric noise estimations

Michel Verleysen, Amaury Lendasse, Nima Reyhani, 2006, ESANN.

Distance estimation in numerical data sets with missing values

Michel Verleysen, Amaury Lendasse, Emil Eirola, 2013, Inf. Sci..

Long-term prediction of time series using NNE-based projection and OP-ELM

Amaury Lendasse, Antti Sorjamaa, Yoan Miché, 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

Bootstrap for Model Selection: Linear Approximation of the Optimism

Michel Verleysen, Amaury Lendasse, Geoffroy Simon, 2003, IWANN.

ELM-SOM: A Continuous Self-Organizing Map for Visualization

Amaury Lendasse, Kaj-Mikael Björk, Anton Akusok, 2018, 2018 International Joint Conference on Neural Networks (IJCNN).

Residual variance estimation using a nearest neighbor statistic

Amaury Lendasse, Francesco Corona, Elia Liitiäinen, 2010, J. Multivar. Anal..

Advances in extreme learning machines (ELM2014)

Amaury Lendasse, Chi-Man Vong, Guang-Bin Huang, 2011, Neurocomputing.

Efficient Detection of Zero-day Android Malware Using Normalized Bernoulli Naive Bayes

Juha Karhunen, Amaury Lendasse, Emil Eirola, 2015, 2015 IEEE Trustcom/BigDataSE/ISPA.

A Two-Stage Methodology Using K-NN and False-Positive Minimizing ELM for Nominal Data Classification

Amaury Lendasse, Rui Nian, Anton Akusok, 2014, Cognitive Computation.

Efficient Skin Segmentation via Neural Networks: HP-ELM and BD-SOM

Amaury Lendasse, Kaj-Mikael Björk, Anton Akusok, 2015, INNS Conference on Big Data.

Time series prediction competition: The CATS benchmark

Erkki Oja, Michel Verleysen, Amaury Lendasse, 2007, Neurocomputing.

Underwater image sparse representation based on bag-of-words and compressed sensing

Amaury Lendasse, Bo He, Yue Shen, 2015, OCEANS 2015 - MTS/IEEE Washington.

Extreme Learning Machines for Multiclass Classification: Refining Predictions with Gaussian Mixture Models

Amaury Lendasse, Kaj-Mikael Björk, Bo He, 2015, IWANN.

Adaptive Ensemble Models of Extreme Learning Machines for Time Series Prediction

Amaury Lendasse, Timo Honkela, Mark van Heeswijk, 2009, ICANN.

Distance Estimation for Incomplete Data by Extreme Learning Machine

Amaury Lendasse, Anton Akusok, Emil Eirola, 2017 .

Embedded spectral descriptors: learning the point-wise correspondence metric via Siamese neural networks

Amaury Lendasse, Zhiyu Sun, Stephen Baek, 2017, J. Comput. Des. Eng..

Nonlinear Time Series Prediction by Weighted Vector Quantization

Michel Verleysen, Amaury Lendasse, Damien François, 2003, International Conference on Computational Science.

Fast variable selection for memetracker phrases time series prediction

Alberto Guillén, Amaury Lendasse, Rui Nian, 2012, PETRA '12.

Forecasting Financial Markets with Classified Tactical Signals

Amaury Lendasse, Yoan Miché, Bertrand Maillet, 2013, ESANN.

A Methodology for Building Regression Models using Extreme Learning Machine: OP-ELM

Amaury Lendasse, Olli Simula, Christian Jutten, 2008, ESANN.

Machine Learning Techniques based on Random Projections

Amaury Lendasse, Benjamin Schrauwen, Yoan Miché, 2010, ESANN.

ELMVIS+: Improved Nonlinear Visualization Technique Using Cosine Distance and Extreme Learning Machines

Amaury Lendasse, Yoan Miche, Kaj-Mikael Björk, 2016 .

On the Curse of Dimensionality in Supervised Learning of Smooth Regression Functions

Amaury Lendasse, Francesco Corona, Elia Liitiäinen, 2011, Neural Processing Letters.

Functional Dimension Reduction for Chemometrics

Tuomas Kärnä, Amaury Lendasse, 2009, Encyclopedia of Artificial Intelligence.

Feature Bagging and Extreme Learning Machines: Machine Learning with Severe Memory Constraints

Amaury Lendasse, Edward Ratner, Kallin Khan, 2020, 2020 International Joint Conference on Neural Networks (IJCNN).

Extreme Learning Machines for Signature Verification

Amaury Lendasse, Anton Akusok, Kaj-Mikael Björk, 2019 .

Using machine learning to identify top predictors for nurses' willingness to report medication errors

Amaury Lendasse, Renjie Hu, Kaj-Mikael Björk, 2020, Array.

Long-Term Prediction of Time Series Using State-Space Models

Amaury Lendasse, Elia Liitiäinen, A. Lendasse, 2006, ICANN.

Manifold learning in local tangent space via extreme learning machine

Amaury Lendasse, Kaj-Mikael Björk, Yue Shen, 2016, Neurocomputing.

Practical Estimation of Mutual Information on Non-Euclidean Spaces

Amaury Lendasse, Wei Ren, Anton Akusok, 2017, CD-MAKE.

SOM-ELM - Self-Organized Clustering using ELM

Amaury Lendasse, Kaj-Mikael Björk, Eric Séverin, 2015, Neurocomputing.

Solving Large Regression Problems using an Ensemble of GPU-accelerated ELMs

Erkki Oja, Amaury Lendasse, Mark van Heeswijk, 2010, ESANN.

Methodology for Behavioral-based Malware Analysis and Detection Using Random Projections and K-Nearest Neighbors Classifiers

Alexander Ilin, Amaury Lendasse, Yoan Miché, 2011, 2011 Seventh International Conference on Computational Intelligence and Security.

Business Plans Classification with Locally Pruned Lazy Learning Models

Michel Verleysen, Amaury Lendasse, Damien François, 2004 .

A fast sonar-based benthic object recognition model via extreme learning machine

Amaury Lendasse, Bo He, Rui Nian, 2015, OCEANS 2015 - MTS/IEEE Washington.

Time series forecasting with SOM and local non-linear models - Application to the DAX30 index prediction

Amaury Lendasse, Geoffroy Simon, Michel Verleysen, 2003 .

Autoregressive time series prediction by means of fuzzy inference systems using nonparametric residual variance estimation

Amaury Lendasse, Angel Barriga Barros, Federico Montesino-Pouzols, 2010, Fuzzy Sets Syst..

Residual variance estimation in machine learning

Michel Verleysen, Amaury Lendasse, Francesco Corona, 2009, Neurocomputing.

A robust nonlinear projection method

Amaury Lendasse, John Aldo Lee, Michel Verleysen, 2000 .

Scikit-ELM: An Extreme Learning Machine Toolbox for Dynamic and Scalable Learning

Amaury Lendasse, Anton Akusok, Kaj-Mikael Björk, 2019 .

SOM+EOF for finding missing values

Amaury Lendasse, Antti Sorjamaa, Bertrand Maillet, 2007, ESANN.

WITHDRAWN: Special issue on “Time series prediction competition: The CATS benchmark”

Erkki Oja, Michel Verleysen, Amaury Lendasse, 2007 .

OP-ELM: Optimally Pruned Extreme Learning Machine

Amaury Lendasse, Olli Simula, Christian Jutten, 2010, IEEE Transactions on Neural Networks.

Regularized extreme learning machine for regression with missing data

Amaury Lendasse, Eric Séverin, Qi Yu, 2013, Neurocomputing.

Gaussian Mixture Models for Time Series Modelling, Forecasting, and Interpolation

Amaury Lendasse, Emil Eirola, A. Lendasse, 2013, IDA.

A robust non-linear projection method

Michel Verleysen, Amaury Lendasse, John Aldo Lee, 2000, ESANN.

A Novel ELM Ensemble for Time Series Prediction

Amaury Lendasse, Kaj-Mikael Björk, Zhen Li, 2018, ELM.

On Distance Mapping from non-Euclidean Spaces to Euclidean Spaces

Amaury Lendasse, Kaj-Mikael Björk, Wei Ren, 2017, CD-MAKE.

Advances in extreme learning machines (ELM2012)

Amaury Lendasse, Qing He, Guang-Bin Huang, 2014, Neurocomputing.

Variable Scaling for Time Series Prediction: Application to the ESTSP'07 and the NN3 Forecasting Competitions

Amaury Lendasse, Elia Liitiäinen, 2007, 2007 International Joint Conference on Neural Networks.

HSR: L1/2-regularized sparse representation for fast face recognition using hierarchical feature selection

Amaury Lendasse, Bo He, Tingting Sun, 2014, Neural Computing and Applications.

Anomaly-Based Intrusion Detection Using Extreme Learning Machine and Aggregation of Network Traffic Statistics in Probability Space

Amaury Lendasse, Aapo Kalliola, Ian Oliver, 2018, Cognitive Computation.

Extraction of intrinsic dimension using CCA - Application to blind sources separation

Michel Verleysen, Amaury Lendasse, Vincent Wertz, 1999, ESANN.

ELM Feature Selection and SOM Data Visualization for Nursing Survey Datasets

Amaury Lendasse, Kaj-Mikael Björk, Renjie Hu, 2020 .

A new application of machine learning in health care

Amaury Lendasse, Kaj-Mikael Björk, Emil Eirola, 2016, PETRA.

Nonlinear projection with curvilinear distances: Isomap versus curvilinear distance analysis

Michel Verleysen, Amaury Lendasse, John Aldo Lee, 2004, Neurocomputing.

Curvilinear Distance Analysis versus Isomap

Michel Verleysen, Amaury Lendasse, John Aldo Lee, 2002, ESANN.

Learning Flow Characteristics Distributions with ELM for Distributed Denial of Service Detection and Mitigation

Amaury Lendasse, Yoan Miche, Tuomas Aura, 2018 .

Linear combination of SOMs for data imputation: Application to financial problems

Amaury Lendasse, Yoan Miche, Eric Séverin, 2009 .

LS-SVM Hyperparameter Selection with a Nonparametric Noise Estimator

Michel Verleysen, Amaury Lendasse, Nima Reyhani, 2005, ICANN.

Advances in extreme learning machines (ELM2014)

Amaury Lendasse, Chi-Man Vong, Kar-Ann Toh, 2011, Neurocomputing.

Statistical fault isolation with PCA

Amaury Lendasse, Guillermo Gómez, A. Lendasse, 2000 .

GPU-accelerated and parallelized ELM ensembles for large-scale regression

Erkki Oja, Amaury Lendasse, Mark van Heeswijk, 2011, Neurocomputing.

Clinical narrative classification using discriminant word embeddings with ELM

Amaury Lendasse, Paula Lauren, Feng Zhang, 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

Online fish tracking with portable smart device for ocean observatory network

Amaury Lendasse, Bo He, Rui Nian, 2017, OCEANS 2017 – Anchorage.

Extreme Learning Machines for VISualization+R: Mastering Visualization with Target Variables

Amaury Lendasse, Anton Akusok, Stephen Baek, 2017, Cognitive Computation.

Optimal Pruned K-Nearest Neighbors: OP-KNN Application to Financial Modeling

Alberto Guillén, Amaury Lendasse, Eric Séverin, 2008, 2008 Eighth International Conference on Hybrid Intelligent Systems.

Fast bootstrap methodology for regression model selection

Michel Verleysen, Amaury Lendasse, Vincent Wertz, 2005, Neurocomputing.

A low-dimensional vector representation for words using an extreme learning machine

Amaury Lendasse, Paula Lauren, Guangzhi Qu, 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

RMSE-ELM: Recursive Model based Selective Ensemble of Extreme Learning Machines for Robustness Improvement pdf

Amaury Lendasse, Bo Han, Bo He, 2014, ArXiv.

Robust Time Series Prediction Using KIII Model

Amaury Lendasse, I. Beliaev, R. Kozma, 2004 .

Embedded Online Fish Detection and Tracking System via YOLOv3 and Parallel Correlation Filter

Amaury Lendasse, Tianhong Yan, Rui Nian, 2018, OCEANS 2018 MTS/IEEE Charleston.

X-SOM and L-SOM: A double classification approach for missing value imputation

Amaury Lendasse, Antti Sorjamaa, Bertrand Maillet, 2010, Neurocomputing.

Brute-force Missing Data Extreme Learning Machine for Predicting Huntington's Disease

Amaury Lendasse, Kaj-Mikael Björk, Anton Akusok, 2017, PETRA.

Mutual information and gamma test for input selection

Amaury Lendasse, Nima Reyhani, Yongnan Ji, 2005, ESANN.

High-Performance ELM for Memory Constrained Edge Computing Devices with Metal Performance Shaders

Amaury Lendasse, Anton Akusok, Kaj-Mikael Björk, 2019 .

Towards a Tomographic Index of Systemic Risk Measures

Amaury Lendasse, Kaj-Mikael Björk, Yoan Miché, 2015, ESANN.

A Web Page Classifier Library Based on Random Image Content Analysis Using Deep Learning

Amaury Lendasse, Kaj-Mikael Björk, Anton Akusok, 2018, PETRA.

Underwater scene search scheme via similarity measure and sparse representation for autonomous underwater vehicle

Amaury Lendasse, Bo He, Tianhong Yan, 2016, OCEANS 2016 MTS/IEEE Monterey.

A rapid weighted median filter based on saliency region for underwater image denoising

Amaury Lendasse, Bo He, Rui Nian, 2015, OCEANS 2015 - MTS/IEEE Washington.

Fast Image Recognition Based on Independent Component Analysis and Extreme Learning Machine

Jing Wang, Amaury Lendasse, Bo Han, 2014, Cognitive Computation.

Applying Mutual Information for Prototype or Instance Selection in Regression Problems

Ginés Rubio, Héctor Pomares, Ignacio Rojas, 2009, ESANN.

Proceedings of ELM-2015 Volume 2: Theory, Algorithms and Applications (II)

Amaury Lendasse, Kezhi Mao, Jiuwen Cao, 2016 .

Extreme Learning Tree pdf

Amaury Lendasse, Anton Akusok, Emil Eirola, 2017, Proceedings in Adaptation, Learning and Optimization.

Extending Extreme Learning Machine with Combination Layer

Amaury Lendasse, Olli Simula, Dusan Sovilj, 2013, IWANN.

Extreme Learning Machine: A Robust Modeling Technique? Yes!

Amaury Lendasse, Olli Simula, Anton Akusok, 2013, IWANN.

Spiking networks for improved cognitive abilities of edge computing devices

Amaury Lendasse, Kaj-Mikael Björk, Anton Akusok, 2019, PETRA.

Forecasting time-series by Kohonen classification

Michel Verleysen, Amaury Lendasse, Philippe Grégoire, 1998, ESANN.

Finding Originally Mislabels with MD-ELM

Amaury Lendasse, Eric Séverin, Anton Akusok, 2014, ESANN.

Comparison of combining methods using Extreme Learning Machines under small sample scenario

Amaury Lendasse, Kaj-Mikael Björk, Dusan Sovilj, 2016, Neurocomputing.

Compressive ELM: Improved Models through Exploiting Time-Accuracy Trade-Offs

Amaury Lendasse, Mark van Heeswijk, Yoan Miché, 2014, EANN.

A Feature Selection Methodology for Steganalysis

Amaury Lendasse, Patrick Bas, Yoan Miché, 2006, MRCS.

An empirical dependence mesaures based on residual variance estimation

Amaury Lendasse, Nima Reyhani, 2007, 2007 9th International Symposium on Signal Processing and Its Applications.

Adaptive Kernel Smoothing Regression for Spatio-Temporal Environmental Datasets

Amaury Lendasse, Federico Montesino-Pouzols, A. Lendasse, 2012, ESANN.

Singular Value Decomposition update and its application to (Inc)-OP-ELM

Amaury Lendasse, Yoan Miche, Maarit Käpylä, 2016, Neurocomputing.

Forecasting electricity consumption using nonlinear projection and self-organizing maps

Michel Verleysen, Amaury Lendasse, John Aldo Lee, 2002, Neurocomputing.

Vector quantization: a weighted version for time-series forecasting

Michel Verleysen, Amaury Lendasse, Damien François, 2005, Future Gener. Comput. Syst..

Extreme learning machines for soybean classification in remote sensing hyperspectral images

Amaury Lendasse, Ramón Moreno, Manuel Graña, 2014, Neurocomputing.

Mutual information for the selection of relevant variables in spectrometric nonlinear modelling pdf

Michel Verleysen, Amaury Lendasse, Fabrice Rossi, 2006, ArXiv.

Minimising the delta test for variable selection in regression problems

Ignacio Rojas, Alberto Guillén, Amaury Lendasse, 2008, Int. J. High Perform. Syst. Archit..

Relevance learning for time series inspection

Amaury Lendasse, Barbara Hammer, Andrej Gisbrecht, 2012, ESANN.

Incremental ELMVIS for unsupervised learning pdf

Amaury Lendasse, Anton Akusok, Stephen Baek, 2019, ArXiv.

Double quantization of the regressor space for long-term time series prediction: method and proof of stability

Michel Verleysen, Amaury Lendasse, Marie Cottrell, 2004, Neural Networks.

Input data reduction for the prediction of financial time series

Michel Verleysen, Amaury Lendasse, John Aldo Lee, 2001, ESANN.

Investigating Feasibility of Active Learning with Image Content on Mobile Devices Using ELM

Amaury Lendasse, Anton Akusok, 2019 .

Pruned lazy learning models for time series prediction

Michel Verleysen, Amaury Lendasse, Antti Sorjamaa, 2005, ESANN.

ELM-SOM+: A continuous mapping for visualization

Amaury Lendasse, Yoan Miche, Kaj-Mikael Björk, 2019, Neurocomputing.

Evolutive Approaches for Variable Selection Using a Non-parametric Noise Estimator

Héctor Pomares, Ignacio Rojas, Alberto Guillén, 2012, Parallel Architectures and Bioinspired Algorithms.

ELM Clustering – Application to Bankruptcy Prediction

Amaury Lendasse, Yoan Miche, Eric Séverin, 2014 .

Evolving fuzzy optimally pruned extreme learning machine for regression problems

Amaury Lendasse, Federico Montesino-Pouzols, A. Lendasse, 2010, Evol. Syst..

Website Classification from Webpage Renders

Amaury Lendasse, Anton Akusok, Kaj-Mikael Björk, 2019 .

Underwater image enhancement strategy with virtual retina model and image quality assessment

Amaury Lendasse, Bo He, Yaomin Wang, 2016, OCEANS 2016 MTS/IEEE Monterey.

High-Performance Extreme Learning Machines: A Complete Toolbox for Big Data Applications

Amaury Lendasse, Kaj-Mikael Björk, Anton Akusok, 2015, IEEE Access.

European Symposium on Times Series Prediction

Amaury Lendasse, Timo Honkela, Olli Simula, 2010, Neurocomputing.

WELM: Extreme Learning Machine with Wavelet Dynamic Co-Movement Analysis in High-Dimensional Time Series

Amaury Lendasse, Yan Song, Rui Nian, 2016 .

Extreme learning machine for missing data using multiple imputations

Amaury Lendasse, Yoan Miche, Kaj-Mikael Björk, 2016, Neurocomputing.

Combination of SOMs value for fast missing value imputation

Amaury Lendasse, Antti Sorjamaa, E. Séverin, 2010 .

Effect of different detrending approaches on computational intelligence models of time series

Amaury Lendasse, Federico Montesino-Pouzols, A. Lendasse, 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

Parameter-free image segmentation with SLIC

Amaury Lendasse, Edward Ratner, Fabian Boemer, 2018, Neurocomputing.

Extreme Learning Machines [Trends & Controversies]

Victor C. M. Leung, Qiang Liu, Jianping Yin, 2013, IEEE Intelligent Systems.

Time series prediction using DirRec strategy

Amaury Lendasse, Antti Sorjamaa, 2006, ESANN.

Meme representations for game agents

Amaury Lendasse, Yew-Soon Ong, Meng-Hiot Lim, 2013, World Wide Web.

Methodology for long-term prediction of time series

Amaury Lendasse, Nima Reyhani, Antti Sorjamaa, 2007, Neurocomputing.

3D object recognition based on a geometrical topology model and extreme learning machine

Amaury Lendasse, Bo He, Rui Nian, 2013, Neural Computing and Applications.

A machine-learning-enhanced hierarchical multiscale method for bridging from molecular dynamics to continua

Amaury Lendasse, Kaj-Mikael Björk, Renjie Hu, 2019, Neural Computing and Applications.

Efficient Parallel Feature Selection for Steganography Problems

Ignacio Rojas, Alberto Guillén, Amaury Lendasse, 2009, IWANN.

Identification of fuzzy models for a glass furnace process

Amaury Lendasse, S. Yurkovich, Vincent Wertz, 1998, Proceedings of the 1998 IEEE International Conference on Control Applications (Cat. No.98CH36104).

Fast approximation of the bootstrap for model selection

Michel Verleysen, Amaury Lendasse, Vincent Wertz, 2003, ESANN.

Data Anonymization as a Vector Quantization Problem: Control Over Privacy for Health Data

Amaury Lendasse, Kaj-Mikael Björk, Anton Akusok, 2016, CD-ARES.

Deep Spectral Descriptors: Learning the point-wise correspondence metric via Siamese deep neural networks pdf

Amaury Lendasse, Stephen Baek, Andrey Gritsenko, 2017, ArXiv.

Solve Classification Tasks with Probabilities. Statistically-Modeled Outputs

Amaury Lendasse, Emil Eirola, Andrey Gritsenko, 2017, HAIS.

Stable underwater image segmentation in high quality via MRF model

Amaury Lendasse, Tianhong Yan, Rui Nian, 2015, OCEANS 2015 - MTS/IEEE Washington.

ROS-ELM: A Robust Online Sequential Extreme Learning Machine for Big Data Analytics

Amaury Lendasse, Yang Liu, Bo He, 2015 .

Evolving fuzzy Optimally Pruned Extreme Learning Machine: A comparative analysis

Amaury Lendasse, Federico Montesino-Pouzols, A. Lendasse, 2010, International Conference on Fuzzy Systems.

Generating Word Embeddings from an Extreme Learning Machine for Sentiment Analysis and Sequence Labeling Tasks

Amaury Lendasse, Paula Lauren, Guangzhi Qu, 2018, Cognitive Computation.

Extreme learning machine towards dynamic model hypothesis in fish ethology research

Amaury Lendasse, Bo He, Rui Nian, 2014, Neurocomputing.

Width optimization of the Gaussian kernels in Radial Basis Function Networks

Michel Verleysen, Amaury Lendasse, John Aldo Lee, 2002, ESANN.

Adding reliability to ELM forecasts by confidence intervals

Amaury Lendasse, Kaj-Mikael Björk, Rui Nian, 2017, Neurocomputing.

Variable selection for regression problems using Gaussian mixture models to estimate mutual information

Juha Karhunen, Amaury Lendasse, Emil Eirola, 2014, 2014 International Joint Conference on Neural Networks (IJCNN).

Ensembles of Locally Linear Models: Application to Bankruptcy Prediction

Amaury Lendasse, Eric Séverin, Qi Yu, 2010, DMIN.

Reliable Steganalysis Using a Minimum Set of Samples and Features

Amaury Lendasse, Olli Simula, Christian Jutten, 2009, EURASIP J. Inf. Secur..

Linear Projection based on Noise Variance Estimation - Application to Spectral Data

Amaury Lendasse, Francesco Corona, 2008, ESANN.

Nonlinear temporal and spatial forecasting: modeling and uncertainty analysis (NoTeS) - MASIT20

Amaury Lendasse, Esa Alhoniemi, Miki Sirola, 2007 .

Ensembles of Local Linear Models for Bankruptcy Analysis and Prediction

Benoît Frénay, Amaury Lendasse, Yoan Miche, 2011 .

A Framework for Privacy Quantification: Measuring the Impact of Privacy Techniques Through Mutual Information, Distance Mapping, and Machine Learning

Amaury Lendasse, Wei Ren, Ian Oliver, 2018, Cognitive Computation.

A novel adaptive restoration for underwater image quality degradation

Amaury Lendasse, Bo He, Rui Nian, 2015, OCEANS 2015 - MTS/IEEE Washington.

RCGA-S/RCGA-SP Methods to Minimize the Delta Test for Regression Tasks

Amaury Lendasse, Rafael Gadea Gironés, Dusan Sovilj, 2009, IWANN.

Combined nonlinear visualization and classification: ELMVIS++C

Amaury Lendasse, Kaj-Mikael Björk, Anton Akusok, 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

MD-ELM: Originally Mislabeled Samples Detection using OP-ELM Model

Amaury Lendasse, Kaj-Mikael Björk, Eric Séverin, 2015, Neurocomputing.

Feature selection for nonlinear models with extreme learning machines

Benoît Frénay, Michel Verleysen, Amaury Lendasse, 2013, Neurocomputing.

Adaptive kernel smoothing regression using vector quantization

Amaury Lendasse, Federico Montesino-Pouzols, A. Lendasse, 2011, 2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS).

Time series forecasting using CCA and Kohonen maps - application to electricity consumption

Michel Verleysen, Amaury Lendasse, John Aldo Lee, 2000, ESANN.

TROP-ELM: A double-regularized ELM using LARS and Tikhonov regularization

Amaury Lendasse, Olli Simula, Mark van Heeswijk, 2011, Neurocomputing.

Input Selection for Long-Term Prediction of Time Series

Amaury Lendasse, Jaakko Hollmén, Jarkko Tikka, 2005, IWANN.

Adaptive and online network intrusion detection system using clustering and Extreme Learning Machines

Amaury Lendasse, Anton Akusok, Yoan Miché, 2017, J. Frankl. Inst..

Sparse Linear Combination of SOMs for Data Imputation: Application to Financial Database

Amaury Lendasse, Eric Séverin, Antti Sorjamaa, 2009, WSOM.

On Nonparametric Residual Variance Estimation

Amaury Lendasse, Francesco Corona, Elia Liitiäinen, 2008, Neural Processing Letters.

LS-SVM functional network for time series prediction

Tuomas Kärnä, Amaury Lendasse, Fabrice Rossi, 2006, ESANN.

On Mutual Information over Non-Euclidean Spaces, Data Mining and Data Privacy Levels

Amaury Lendasse, Yoan Miche, Kaj-Mikael Björk, 2016 .

Time Series Benchmarking Competition: The CATS Benchmark

Erkki Oja, Amaury Lendasse, Olli Simula, 2004 .

Underwater non-rigid 3D shape reconstruction via structure from motion for fish ethology research

Amaury Lendasse, Bo He, Rui Nian, 2016, OCEANS 2016 MTS/IEEE Monterey.

Mutual Information and k-Nearest Neighbors Approximator for Time Series Prediction

Amaury Lendasse, Antti Sorjamaa, Jin Hao, 2005, ICANN.

A boundary corrected expansion of the moments of nearest neighbor distributions

Amaury Lendasse, Francesco Corona, Elia Liitiäinen, 2010, Random Struct. Algorithms.

The delta test: The 1-NN estimator as a feature selection criterion

Michel Verleysen, Amaury Lendasse, Francesco Corona, 2014, 2014 International Joint Conference on Neural Networks (IJCNN).

Ensemble Modeling with a Constrained Linear System of Leave-One-Out Outputs

Michel Verleysen, Amaury Lendasse, Olli Simula, 2010, ESANN.

X-SOM and L-SOM: a nested approach for missing value imputation

Amaury Lendasse, Antti Sorjamaa, Bertrand Maillet, 2009, ESANN.

Ensemble classifiers for bankruptcy prediction

Amaury Lendasse, Yoan Miche, Qi Yu, 2010 .

Nearest Neighbor Distributions and Noise Variance Estimation

Amaury Lendasse, Francesco Corona, Elia Liitiäinen, 2007, ESANN.

Advanced query strategies for Active Learning with Extreme Learning Machines

Amaury Lendasse, Anton Akusok, Emil Eirola, 2017, ESANN.

Input and Structure Selection for k-NN Approximator

Amaury Lendasse, Nima Reyhani, Antti Sorjamaa, 2005, IWANN.

Arbitrary Category Classification of Websites Based on Image Content

Juha Karhunen, Amaury Lendasse, Kaj-Mikael Björk, 2015, IEEE Computational Intelligence Magazine.

Bankruptcy Prediction with Missing Data

Amaury Lendasse, Yoan Miche, Eric Séverin, 2011 .

Non-parametric Residual Variance Estimation in Supervised Learning

Amaury Lendasse, Francesco Corona, Elia Liitiäinen, 2007, IWANN.

Minimal Learning Machine: A New Distance-Based Method for Supervised Learning

Amaury Lendasse, Olli Simula, Francesco Corona, 2013, IWANN.

Segmentation of Sidescan Sonar Imagery Using Markov Random Fields and Extreme Learning Machine

Amaury Lendasse, Yan Song, Bo He, 2019, IEEE Journal of Oceanic Engineering.

New method for instance or prototype selection using mutual information in time series prediction

Ginés Rubio, Héctor Pomares, Ignacio Rojas, 2010, Neurocomputing.

A faster model selection criterion for OP-ELM and OP-KNN: Hannan-Quinn criterion

Amaury Lendasse, Yoan Miché, Y. Miché, 2009, ESANN.

Image-based Classification of Websites

Amaury Lendasse, Yoan Miche, Anton Akusok, 2013 .

Multiple-Output Modelling for Multi-Step-Ahead Forecasting

Amaury Lendasse, Antti Sorjamaa, Gianluca Bontempi, 2009 .

Visualizing dependencies of spectral features using mutual information

Amaury Lendasse, Barbara Hammer, Andrej Gisbrecht, 2013, ESANN.

Data Preprocessing and Model Design for Medicine Problems

Alberto Guillén, Amaury Lendasse, Guilherme De A. Barreto, 2013, Comput. Math. Methods Medicine.

A modified Lanczos Algorithm for fast regularization of extreme learning machines

Amaury Lendasse, Kaj-Mikael Björk, Edward Ratner, 2020, Neurocomputing.

Long-term prediction of time series by combining direct and MIMO strategies

Amaury Lendasse, Antti Sorjamaa, Gianluca Bontempi, 2009, 2009 International Joint Conference on Neural Networks.

Discriminant document embeddings with an extreme learning machine for classifying clinical narratives

Amaury Lendasse, Paula Lauren, Feng Zhang, 2018, Neurocomputing.

ELMVIS: a Nonlinear Visualization Technique using Random Permutations and Extreme Learning Machine

Amaury Lendasse, Yoan Miche, Rui Nian, 2013 .

Fast Feature Selection in a GPU Cluster Using the Delta Test

Héctor Pomares, Ignacio Rojas, Alberto Guillén, 2014, Entropy.

Minimal Learning Machine: A novel supervised distance-based approach for regression and classification

Amaury Lendasse, Francesco Corona, Guilherme De A. Barreto, 2015, Neurocomputing.

Une méthodologie pour la Sélection de Variables pour la Stéganalyse A Feature Selection Methodology for Steganalysis

Amaury Lendasse, Yoan Miche, Olli Simula, 2009 .

ELMVIS: A nonlinear visualization technique using random permutations and ELMs

Amaury Lendasse, Yoan Miche, Rui Nian, 2013 .

A SOM-based approach to estimating product properties from spectroscopic measurements

Amaury Lendasse, Francesco Corona, Roberto Baratti, 2009, Neurocomputing.

An improved methodology for filling missing values in spatiotemporal climate data set

Amaury Lendasse, Eric Deleersnijder, Antti Sorjamaa, 2010 .

Per-sample prediction intervals for extreme learning machines

Amaury Lendasse, Kaj-Mikael Björk, Anton Akusok, 2018, International Journal of Machine Learning and Cybernetics.

Probabilistic Methods for Multiclass Classification Problems

Amaury Lendasse, Emil Eirola, Edward Ratner, 2016 .

Time series forecasting: Obtaining long term trends with self-organizing maps

Michel Verleysen, Amaury Lendasse, Marie Cottrell, 2005, Pattern Recognit. Lett..

Mixture of Gaussians for distance estimation with missing data

Amaury Lendasse, Vincent Vandewalle, Christophe Biernacki, 2014, Neurocomputing.

LARSEN-ELM: Selective ensemble of extreme learning machines using LARS for blended data

Amaury Lendasse, Bo Han, Bo He, 2015, Neurocomputing.

Domain Adaption via Feature Selection on Explicit Feature Map

Amaury Lendasse, Wan-Yu Deng, Qing-Hua Zheng, 2019, IEEE Transactions on Neural Networks and Learning Systems.

A shadow-removal based saliency map for point feature detection of underwater objects

Amaury Lendasse, Tianhong Yan, Rui Nian, 2015, OCEANS 2015 - MTS/IEEE Washington.

Fast Bootstrap for Model Structure Selection

Michel Verleysen, Amaury Lendasse, Vincent Wertz, 2003 .

Gaussian derivative models and ensemble extreme learning machine for texture image classification

Yan Song, Amaury Lendasse, Bo He, 2018, Neurocomputing.

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.

Brain MRI morphological patterns extraction tool based on Extreme Learning Machine and majority vote classification

Amaury Lendasse, Manuel Graña, Kaj-Mikael Björk, 2016, Neurocomputing.

Advantages of Using Feature Selection Techniques on Steganalysis Schemes

Amaury Lendasse, Olli Simula, Christian Jutten, 2007, IWANN.

Using the Delta Test for Variable Selection

Michel Verleysen, Amaury Lendasse, Francesco Corona, 2008, ESANN.

Time Series Forecasting using CCA and Kohonen Maps - Applications to Electricity Consumption

Amaury Lendasse, Vincent Wertz, John Aldo Lee, 2000 .

Data Fusion Using OPELM for Low-Cost Sensors in AUV

Amaury Lendasse, Tianhong Yan, Pengfei Lv, 2018 .

OP-ELM: Theory, Experiments and a Toolbox

Amaury Lendasse, Antti Sorjamaa, Yoan Miché, 2008, ICANN.

EM-algorithm for training of state-space models with application to time series prediction

Amaury Lendasse, Nima Reyhani, Elia Liitiäinen, 2006, ESANN.

Locating Anomalies Using Bayesian Factorizations and Masks

Li Yao, Amaury Lendasse, Francesco Corona, 2011, ESANN.

Direct and Recursive Prediction of Time Series Using Mutual Information Selection

Amaury Lendasse, Nima Reyhani, Yongnan Ji, 2005, IWANN.

Time Series Prediction as a Problem of Missing Values: Application to ESTSP2007 and NN3 Competition Benchmarks

Amaury Lendasse, Antti Sorjamaa, 2007, 2007 International Joint Conference on Neural Networks.

OP-KNN: Method and Applications

Alberto Guillén, Amaury Lendasse, Eric Séverin, 2010, Adv. Artif. Neural Syst..

Fast bootstrap for least-square support vector machines

Michel Verleysen, Amaury Lendasse, Vincent Wertz, 2004, ESANN.

Deformable Surface Registration with Extreme Learning Machines

Amaury Lendasse, Yoan Miche, Zhiyu Sun, 2017, Proceedings in Adaptation, Learning and Optimization.

Analysis of Fast Input Selection: Application in Time Series Prediction

Amaury Lendasse, Jaakko Hollmén, Jarkko Tikka, 2006, ICANN.

Fuzzy inference based autoregressors for time series prediction using nonparametric residual variance estimation

Amaury Lendasse, Angel Barriga, Federico Montesino-Pouzols, 2008, 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence).

Fast Face Recognition Via Sparse Coding and Extreme Learning Machine

Amaury Lendasse, Bo He, Rui Nian, 2013, Cognitive Computation.

Bankruptcy prediction using Extreme Learning Machine and financial expertise

Amaury Lendasse, Eric Séverin, Qi Yu, 2014, Neurocomputing.

Avantages de la Sélection de Caractéristiques pour la Stéganalyse

Amaury Lendasse, Yoan Miche, Olli Simula, 2007 .

Ensemble delta test-extreme learning machine (DT-ELM) for regression

Amaury Lendasse, Eric Séverin, Bo He, 2014, Neurocomputing.

Random Projection Method for Scalable Malware Classification

Alexander Ilin, Amaury Lendasse, Yoan Miche, 2011 .

Local Linear Regression for Soft-Sensor Design with Application to an Industrial Deethanizer

Zhanxing Zhu, Amaury Lendasse, Jose A. Romagnoli, 2011 .

Interpreting Extreme Learning Machine as an Approximation to an Infinite Neural Network

Amaury Lendasse, Yoan Miché, Elina Parviainen, 2010, KDIR.

Long-term time series forecasting using self-organizing maps : the double vector quantization method

Michel Verleysen, Amaury Lendasse, Marie Cottrell, 2003 .

Practical Estimation of Missing Phosphorus Values in Pyhäjärvi Lake Data

Amaury Lendasse, Anton Akusok, Alexander Grigorievskiy, 2013 .

Long-term time series prediction using OP-ELM

Amaury Lendasse, Eric Séverin, Yoan Miché, 2014, Neural Networks.

Advances in extreme learning machines (ELM2014)

Amaury Lendasse, Qing He, Guang-Bin Huang, 2011, Neurocomputing.

Underwater obstacle detection via relative total variation and joint guided filtering for autonomous underwater vehicles

Ying Chen, Amaury Lendasse, Jia Li, 2017, OCEANS 2017 – Anchorage.

Auto-detection of Anisakid larvae in Cod Fillets by UV fluorescent imaging with OS-ELM

Amaury Lendasse, Rui Nian, Wenqiang Cai, 2015, TENCON 2015 - 2015 IEEE Region 10 Conference.

Validating Untrained Human Annotations Using Extreme Learning Machines

Amaury Lendasse, Anton Akusok, Kaj-Mikael Björk, 2019 .

Model Selection with Cross-Validations and Bootstraps - Application to Time Series Prediction with RBFN Models

Michel Verleysen, Amaury Lendasse, Vincent Wertz, 2003, ICANN.

Forecasting Financial Time Series through Intrinsic Dimension Estimation and Non-Linear Data Projection

Michel Verleysen, Amaury Lendasse, Eric de Bodt, 1999, IWANN.

A Multi-ELM Model for Incomplete Data

Amaury Lendasse, Renjie Hu, Baichuan Chi, 2021, The European Symposium on Artificial Neural Networks.

NNBMSS: a Novel and Fast Method for Model Structure Selection

Amaury Lendasse, Kallin Khan, Edward R. Ratner, 2021, The European Symposium on Artificial Neural Networks.

Machine Learning for Measuring and Analyzing Online Social Communications

Amaury Lendasse, Dan S. Wallach, Christopher Bronk, 2021, ESANN.

Handwriting features based detection of fake signatures

Amaury Lendasse, Kaj-Mikael Björk, Anton Akusok, 2021, PETRA.

Extracting relevant features of steganographic schemes by feature selection techniques

Amaury Lendasse, Yoan Miche, Olli Simula, 2007 .

14th IEEE international conference on Trust, Security, and Privacy in Computing and Communications (TrustCom), Helsinki, Finland, August 20-22, 2015

Juha Karhunen, Amaury Lendasse, Yoan Miche, 2015 .

Underwater object detection with efficient shadow-removal for side scan sonar images

Amaury Lendasse, Bo He, Rui Nian, 2016, OCEANS 2016 - Shanghai.

A continuous regression function for the Delaunay calibration method

Amaury Lendasse, Francesco Corona, Roberto Baratti, 2010 .

Ensemble KNNs for Bankruptcy Prediction

Amaury Lendasse, Eric Séverin, A. Lendasse, 2009 .

A Global Methodology for Variable Selection Application to Financial Modeling

Amaury Lendasse, Eric Séverin, Qi Yu, 2007 .

A depth estimation model from a single underwater image with non-uniform illumination correction

Amaury Lendasse, Bo He, Rui Nian, 2017, OCEANS 2017 - Aberdeen.

Time series forecasting with SOM and local non-linear models-Application to the DAX 30 index prediction

Michel Verleysen, Amaury Lendasse, Geoffroy Simon, 2003 .

Underwater 3D object reconstruction with multiple views in video stream via structure from motion

Amaury Lendasse, Bo He, Rui Nian, 2016, OCEANS 2016 - Shanghai.

Approximation by Radial-Basis Function networks - Application to option pricing

Michel Verleysen, Amaury Lendasse, Vincent Wertz, 2003 .

On the statistical estimation of Rényi entropies

Amaury Lendasse, Francesco Corona, Elia Liitiainen, 2009, 2009 IEEE International Workshop on Machine Learning for Signal Processing.

Underwater object tracking strategy via multi-scale retinex and partial least squares analysis

Amaury Lendasse, Bo He, Rui Nian, 2017, OCEANS 2017 - Aberdeen.

Using multiple re-embeddings for quantitative steganalysis and image reliability estimation

Amaury Lendasse, Yoan Miche, Patrick Bas, 2010 .

Predicting Huntington’s Disease: Extreme Learning Machine with Missing Values

Amaury Lendasse, Kaj-Mikael Björk, Anton Akusok, 2018 .

Prediction as a Problem of Missing Values

Amaury Lendasse, Antti Sorjamaa, 2006 .

Combination of SOMs for Fast Missing Value Imputation

Amaury Lendasse, Antti Sorjamaa, A. Lendasse, 2010 .