D. Anguita

Luca Oneto, Alice Consilvio, Nicola Sacco, 2019, 2019 6th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS).

Multilayer Graph Node Kernels: Stacking While Maintaining Convexity

Alessandro Sperduti, Davide Anguita, Luca Oneto, 2018, Neural Processing Letters.

Constraint-Aware Data Analysis on Mobile Devices: An Application to Human Activity Recognition on Smartphones

Davide Anguita, Luca Oneto, J. L. Ortiz, 2017 .

Quantum computing and supervised machine learning

Davide Anguita, Luca Oneto, Sandro Ridella, 2017 .

Semi-supervised Learning for Affective Common-Sense Reasoning

Davide Anguita, Luca Oneto, Erik Cambria, 2017, Cognitive Computation.

Learning Hardware-Friendly Classifiers Through Algorithmic Stability

Davide Anguita, Luca Oneto, Sandro Ridella, 2016, ACM Trans. Embed. Comput. Syst..

Performance assessment and uncertainty quantification of predictive models for smart manufacturing systems

Davide Anguita, Luca Oneto, Ilenia Orlandi, 2015, 2015 IEEE International Conference on Big Data (Big Data).

A Deep Connection Between the Vapnik–Chervonenkis Entropy and the Rademacher Complexity

Davide Anguita, Luca Oneto, Sandro Ridella, 2014, IEEE Transactions on Neural Networks and Learning Systems.

Some results about the Vapnik-Chervonenkis entropy and the rademacher complexity

Davide Anguita, Luca Oneto, Sandro Ridella, 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

Model selection for support vector machines: Advantages and disadvantages of the Machine Learning Theory

Davide Anguita, Luca Oneto, Sandro Ridella, 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

K-Fold Cross Validation for Error Rate Estimate in Support Vector Machines

Davide Anguita, Sandro Ridella, Alessandro Ghio, 2009, DMIN.

Mapping LSSVM on digital hardware

D. Anguita, A. Zorat, A. Boni, 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

The ISAAC server: a proposal for smart algorithms delivering

Davide Anguita, Fabio Rivieccio, Anna Marina Scapolla, 2003 .

Emerging trends in machine learning: beyond conventional methods and data

Davide Anguita, Luca Oneto, Nicolò Navarin, 2018, ESANN.

Nature Inspiration for Support Vector Machines

Davide Anguita, Dario Sterpi, D. Anguita, 2006, KES.

Using Unsupervised Analysis to Constrain Generalization Bounds for Support Vector Classifiers

Davide Anguita, Sandro Ridella, Paolo Gastaldo, 2010, IEEE Transactions on Neural Networks.

A local Vapnik-Chervonenkis complexity

Davide Anguita, Luca Oneto, Sandro Ridella, 2016, Neural Networks.

Big Data Analytics in the Cloud: Spark on Hadoop vs MPI/OpenMP on Beowulf

Davide Anguita, Luca Oneto, Jorge Luis Reyes-Ortiz, 2015, INNS Conference on Big Data.

SVM performance assessment for the control of injection moulding processes and plasticating extrusion

Davide Anguita, Andrea Boni, L. Tagliafico, 2002, Int. J. Syst. Sci..

PAC-bayesian analysis of distribution dependent priors: Tighter risk bounds and stability analysis

Davide Anguita, Luca Oneto, Sandro Ridella, 2016, Pattern Recognit. Lett..

Can machine learning explain human learning?

Davide Anguita, Luca Oneto, Matthias Rauterberg, 2016, Neurocomputing.

Learning Resource-Aware Classifiers for Mobile Devices: From Regularization to Energy Efficiency

Davide Anguita, Luca Oneto, Sandro Ridella, 2015, Neurocomputing.

Fully Empirical and Data-Dependent Stability-Based Bounds

Davide Anguita, Luca Oneto, Sandro Ridella, 2015, IEEE Transactions on Cybernetics.

Fast convergence of extended Rademacher Complexity bounds

Davide Anguita, Luca Oneto, Sandro Ridella, 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

Global Rademacher Complexity Bounds: From Slow to Fast Convergence Rates

Davide Anguita, Luca Oneto, Sandro Ridella, 2015, Neural Processing Letters.

Local Rademacher Complexity: Sharper risk bounds with and without unlabeled samples

Davide Anguita, Luca Oneto, Sandro Ridella, 2015, Neural Networks.

Unlabeled patterns to tighten Rademacher complexity error bounds for kernel classifiers

Davide Anguita, Luca Oneto, Sandro Ridella, 2014, Pattern Recognit. Lett..

An improved analysis of the Rademacher data-dependent bound using its self bounding property

Davide Anguita, Luca Oneto, Sandro Ridella, 2013, Neural Networks.

In-Sample and Out-of-Sample Model Selection and Error Estimation for Support Vector Machines

Davide Anguita, Luca Oneto, Sandro Ridella, 2012, IEEE Transactions on Neural Networks and Learning Systems.

The Impact of Unlabeled Patterns in Rademacher Complexity Theory for Kernel Classifiers

Davide Anguita, Luca Oneto, Sandro Ridella, 2011, NIPS.

Maximal Discrepancy for Support Vector Machines

Davide Anguita, Sandro Ridella, Alessandro Ghio, 2011, ESANN.

Support Vector Motion Clustering

Andrea Cavallaro, Davide Anguita, Fabio Poiesi, 2017, IEEE Transactions on Circuits and Systems for Video Technology.

Associative structures for vision

Davide Anguita, Rodolfo Zunino, Giancarlo Parodi, 1994, Multidimens. Syst. Signal Process..

Transputer-based architectures for associative image classification

Davide Anguita, Rodolfo Zunino, Giancarlo Parodi, 1991, Proceedings of the Third IEEE Symposium on Parallel and Distributed Processing.

Data-Driven Photovoltaic Power Production Nowcasting and Forecasting for Polygeneration Microgrids

Federico Delfino, Michela Robba, Davide Anguita, 2018, IEEE Systems Journal.

Advanced Analytics for Train Delay Prediction Systems by Including Exogenous Weather Data

Davide Anguita, Luca Oneto, Renzo Canepa, 2016, 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA).

Machine learning approaches for improving condition-based maintenance of naval propulsion plants

Davide Anguita, Luca Oneto, Massimo Figari, 2016 .

Ship efficiency forecast based on sensors data collection: Improving numerical models through data analytics

Davide Anguita, Luca Oneto, Francesco Baldi, 2015, OCEANS 2015 - Genova.

The 'K' in K-fold Cross Validation

Davide Anguita, Luca Oneto, Sandro Ridella, 2012, ESANN.

Energy Load Forecasting Using Empirical Mode Decomposition and Support Vector Regression

Davide Anguita, Alessandro Ghio, Luca Ghelardoni, 2013, IEEE Transactions on Smart Grid.

Human Activity Recognition on Smartphones Using a Multiclass Hardware-Friendly Support Vector Machine

Davide Anguita, Luca Oneto, Alessandro Ghio, 2012, IWAAL.

Quantum optimization for training support vector machines

Davide Anguita, Sandro Ridella, Rodolfo Zunino, 2003, Neural Networks.

In-sample Model Selection for Trimmed Hinge Loss Support Vector Machine

Davide Anguita, Luca Oneto, Sandro Ridella, 2012, Neural Processing Letters.

Tikhonov, Ivanov and Morozov regularization for support vector machine learning

S. Ridella, L. Oneto, D. Anguita, 2016, Machine Learning.

Optimizing Fuel Consumption in Thrust Allocation for Marine Dynamic Positioning Systems

Davide Anguita, Luca Oneto, Andrea Coraddu, 2021, IEEE Transactions on Automation Science and Engineering.

A support vector machine with integer parameters

Davide Anguita, Sandro Ridella, Alessandro Ghio, 2008, Neurocomputing.

Condition-based maintenance of naval propulsion systems: Data analysis with minimal feedback

Davide Anguita, Luca Oneto, Andrea Coraddu, 2018, Reliab. Eng. Syst. Saf..

Do we really need a new theory to understand over-parameterization?

S. Ridella, L. Oneto, D. Anguita, 2023, Neurocomputing.

Vessels fuel consumption forecast and trim optimisation: A data analytics perspective

Davide Anguita, Luca Oneto, Francesco Baldi, 2017 .

Transition-Aware Human Activity Recognition Using Smartphones

Davide Anguita, Luca Oneto, Albert Samà, 2016, Neurocomputing.

Differential privacy and generalization: Sharper bounds with applications

Davide Anguita, Luca Oneto, Sandro Ridella, 2017, Pattern Recognit. Lett..

Training Computationally Efficient Smartphone-Based Human Activity Recognition Models

Davide Anguita, Luca Oneto, Alessandro Ghio, 2013, ICANN.

Understanding Violin Players’ Skill Level Based on Motion Capture: a Data-Driven Perspective

Davide Anguita, Luca Oneto, Antonio Camurri, 2020, Cognitive Computation.

Mining Big Data with Random Forests

L. Oneto, D. Anguita, Alessandro Lulli, 2019, Cognitive Computation.

SLT-Based ELM for Big Social Data Analysis

Davide Anguita, Luca Oneto, Erik Cambria, 2017, Cognitive Computation.

Novel efficient technologies in Europe for axle bearing condition monitoring – the MAXBE project

Davide Anguita, Frederik Vermeulen, Rui Calçada, 2016 .

Unintrusive Monitoring of Induction Motors Bearings via Deep Learning on Stator Currents

Davide Anguita, Luca Oneto, Andrea Coraddu, 2018, INNS Conference on Big Data.

Condition Based Maintenance in Railway Transportation Systems Based on Big Data Streaming Analysis

Davide Anguita, Luca Oneto, Emanuele Fumeo, 2015, INNS Conference on Big Data.

A digital architecture for support vector machines: theory, algorithm, and FPGA implementation

Davide Anguita, Andrea Boni, Sandro Ridella, 2003, IEEE Trans. Neural Networks.

A Learning Analytics Approach to Correlate the Academic Achievements of Students with Interaction Data from an Educational Simulator

Davide Anguita, Luca Oneto, Matthias Rauterberg, 2015, EC-TEL.

Statistical Learning Theory and ELM for Big Social Data Analysis

Davide Anguita, Luca Oneto, Erik Cambria, 2016, IEEE Computational Intelligence Magazine.

Dynamic Delay Predictions for Large-Scale Railway Networks: Deep and Shallow Extreme Learning Machines Tuned via Thresholdout

Davide Anguita, Luca Oneto, Renzo Canepa, 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

Advances in learning analytics and educational data mining

Davide Anguita, Luca Oneto, Matthias Rauterberg, 2015, ESANN.

Energy Efficient Smartphone-Based Activity Recognition using Fixed-Point Arithmetic

Davide Anguita, Luca Oneto, Alessandro Ghio, 2013, J. Univers. Comput. Sci..

Object Oriented Design of a Simulator for Large BP Neural Networks

Jean-Marc Adamo, Davide Anguita, J. Adamo, 1995, IWANN.

Learning in large neural networks

Davide Anguita, Rodolfo Zunino, Filippo Passaggio, 1995, HPCN Europe.

Spectral Analysis of Electricity Demand Using Hilbert–Huang Transform

Davide Anguita, Joaquin Luque, Robert Denda, 2020, Sensors.

Human Activity Recognition on Smartphones with Awareness of Basic Activities and Postural Transitions

Davide Anguita, Luca Oneto, Alessandro Ghio, 2014, ICANN.

A Survey of old and New Results for the Test Error Estimation of a Classifier

Davide Anguita, Sandro Ridella, Alessandro Ghio, 2013, J. Artif. Intell. Soft Comput. Res..

Test error bounds for classifiers: A survey of old and new results

Davide Anguita, Sandro Ridella, Alessandro Ghio, 2011, 2011 IEEE Symposium on Foundations of Computational Intelligence (FOCI).

Learn and Visually Explain Deep Fair Models: an Application to Face Recognition

Davide Anguita, Luca Oneto, Nicolò Navarin, 2021, 2021 International Joint Conference on Neural Networks (IJCNN).

Learning With Kernels: A Local Rademacher Complexity-Based Analysis With Application to Graph Kernels

Alessandro Sperduti, Davide Anguita, Luca Oneto, 2018, IEEE Transactions on Neural Networks and Learning Systems.

Measuring the expressivity of graph kernels through Statistical Learning Theory

Alessandro Sperduti, Davide Anguita, Luca Oneto, 2017, Neurocomputing.

A dynamic, interpretable, and robust hybrid data analytics system for train movements in large-scale railway networks

L. Oneto, D. Anguita, Renzo Canepa, 2019, International Journal of Data Science and Analytics.

Maximal Discrepancy vs. Rademacher Complexity for error estimation

Davide Anguita, Luca Oneto, Sandro Ridella, 2011, ESANN.

Global Rademacher Complexity Bounds: From Slow to Fast Convergence Rates

S. Ridella, L. Oneto, D. Anguita, 2016, Neural Processing Letters.

Out-of-Sample Error Estimation: The Blessing of High Dimensionality

Davide Anguita, Luca Oneto, Sandro Ridella, 2014, 2014 IEEE International Conference on Data Mining Workshop.

The benefits of adversarial defense in generalization

S. Ridella, L. Oneto, D. Anguita, 2022, Neurocomputing.

Local Rademacher Complexity Machine

Davide Anguita, Luca Oneto, Sandro Ridella, 2019, ESANN.

Randomized learning: Generalization performance of old and new theoretically grounded algorithms

Davide Anguita, Luca Oneto, Sandro Ridella, 2018, Neurocomputing.

Measuring the Expressivity of Graph Kernels through the Rademacher Complexity

Alessandro Sperduti, Davide Anguita, Luca Oneto, 2016, ESANN.

Advances in Learning with Kernels: Theory and Practice in a World of growing Constraints

Davide Anguita, Luca Oneto, Fabio Aiolli, 2016, ESANN.

A Learning Machine with a Bit-Based Hypothesis Space

Davide Anguita, Luca Oneto, Sandro Ridella, 2013, ESANN.

Optical wireless underwater communication for AUV: Preliminary simulation and experimental results

Davide Anguita, Davide Brizzolara, Giancarlo Parodi, 2011, OCEANS 2011 IEEE - Spain.

Smart underwater wireless sensor networks

D. Anguita, A. Ghio, L. Ghelardoni, 2012, 2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel.

Optical wireless communication for underwater Wireless Sensor Networks: Hardware modules and circuits design and implementation

D Anguita, D Brizzolara, G Parodi, 2010, OCEANS 2010 MTS/IEEE SEATTLE.

VHDL modeling of PHY and MAC Layer modules for underwater optical wireless communication

Davide Anguita, Davide Brizzolara, Giancarlo Parodi, 2010, Proceedings of Papers 5th European Conference on Circuits and Systems for Communications (ECCSC'10).

A support vector machine classifier from a bit-constrained, sparse and localized hypothesis space

Davide Anguita, Luca Oneto, Sandro Ridella, 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

Rademacher Complexity and Structural Risk Minimization: An Application to Human Gene Expression Datasets

Davide Anguita, Luca Oneto, Sandro Ridella, 2012, ICANN.

Selecting the hypothesis space for improving the generalization ability of Support Vector Machines

Davide Anguita, Luca Oneto, Sandro Ridella, 2011, The 2011 International Joint Conference on Neural Networks.

Testing the Augmented Binary Multiclass SVM on Microarray Data

Davide Anguita, Sandro Ridella, Dario Sterpi, 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

Train Delay Prediction Systems: A Big Data Analytics Perspective

Davide Anguita, Luca Oneto, Renzo Canepa, 2017, Big Data Res..

Using Variable Neighborhood Search to improve the Support Vector Machine performance in embedded automotive applications

Enrique Alba, Davide Anguita, Sandro Ridella, 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

Evaluating the Generalization Ability of Support Vector Machines through the Bootstrap

Davide Anguita, Andrea Boni, Sandro Ridella, 2000, Neural Processing Letters.

K-fold generalization capability assessment for support vector classifiers

D. Anguita, S. Ridella, F. Rivieccio, 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

A FPGA Core Generator for Embedded Classification Systems

Davide Anguita, Sandro Ridella, Alessandro Ghio, 2011, J. Circuits Syst. Comput..

A learning machine for resource-limited adaptive hardware

Davide Anguita, Alessandro Ghio, Stefano Pischiutta, 2007, Second NASA/ESA Conference on Adaptive Hardware and Systems (AHS 2007).

Circuital implementation of support vector machines

Davide Anguita, Sandro Ridella, Stefano Rovetta, 1998 .

Shrinkage learning to improve SVM with hints

Davide Anguita, Luca Oneto, Sandro Ridella, 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

In-sample model selection for Support Vector Machines

Davide Anguita, Luca Oneto, Sandro Ridella, 2011, The 2011 International Joint Conference on Neural Networks.

Towards analog and digital hardware for support vector machines

Davide Anguita, Andrea Boni, D. Anguita, 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

Fast training of Support Vector Machines for regression

Davide Anguita, Andrea Boni, Stefano Pace, 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

A Hardware-friendly Support Vector Machine for Embedded Automotive Applications

Davide Anguita, Sandro Ridella, Alessandro Ghio, 2007, 2007 International Joint Conference on Neural Networks.

Improved neural network for SVM learning

Davide Anguita, Andrea Boni, D. Anguita, 2002, IEEE Trans. Neural Networks.

Machine learning for wear forecasting of naval assets for condition-based maintenance applications

Davide Anguita, Luca Oneto, Alessandro Ghio, 2015, 2015 International Conference on Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehicles (ESARS).

Support vector machines for interval discriminant analysis

Davide Anguita, Luis González Abril, Juan Antonio Ortega, 2008, Neurocomputing.

Vessels Fuel Consumption: A Data Analytics Perspective to Sustainability

Davide Anguita, Luca Oneto, Francesco Baldi, 2018 .

Structural Risk Minimization and Rademacher Complexity for Regression

Davide Anguita, Luca Oneto, Sandro Ridella, 2012, ESANN.

Multilayer Graph Node Kernels: Stacking While Maintaining Convexity

A. Sperduti, L. Oneto, D. Anguita, 2018, Neural Processing Letters.

A Novel Procedure for Training L1-L2 Support Vector Machine Classifiers

Davide Anguita, Luca Oneto, Sandro Ridella, 2013, ICANN.

Do We Really Need a New Theory to Understand the Double-Descent?

S. Ridella, L. Oneto, D. Anguita, 2022, ESANN.

Delay Prediction System for Large-Scale Railway Networks Based on Big Data Analytics

Davide Anguita, Luca Oneto, Renzo Canepa, 2016, INNS Conference on Big Data.

Towards Randomized Algorithms and Models that We Can Trust: a Theoretical Perspective

S. Ridella, L. Oneto, D. Anguita, 2023, ESANN.

Deep graph node kernels: A convex approach

Alessandro Sperduti, Davide Anguita, Luca Oneto, 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

Learning Hardware Friendly Classifiers Through Algorithmic Risk Minimization

Davide Anguita, Luca Oneto, L. Oneto, 2015, Advances in Neural Networks.

Smartphone battery saving by bit-based hypothesis spaces and local Rademacher Complexities

Davide Anguita, Luca Oneto, Sandro Ridella, 2014, 2014 International Joint Conference on Neural Networks (IJCNN).

The effect of quantization on support vector machines with Gaussian kernel

D. Anguita, G. Bozza, D. Anguita, 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

Learning algorithm for nonlinear support vector machines suited for digital VLSI

Davide Anguita, Sandro Ridella, Andrea Boni, 1999 .

Mitigating Robustness Bias: Theoretical Results and Empirical Evidences

L. Oneto, D. Anguita, Danilo Franco, 2023, ESANN.

Dropout Prediction at University of Genoa: a Privacy Preserving Data Driven Approach

Davide Anguita, Luca Oneto, Gianvittorio Luria, 2017, ESANN.

Large-Scale Railway Networks Train Movements: A Dynamic, Interpretable, and Robust Hybrid Data Analytics System

Davide Anguita, Luca Oneto, Simone Petralli, 2018, 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA).

Learn and Visually Explain Deep Fair Models: an Application to Face Recognition

L. Oneto, D. Anguita, Danilo Franco, 2021, 2021 International Joint Conference on Neural Networks (IJCNN).

Neural network learning for analog VLSI implementations of support vector machines: a survey

Davide Anguita, Andrea Boni, D. Anguita, 2003, Neurocomputing.

Support vector machines and strictly positive definite kernel: The regularization hyperparameter is more important than the kernel hyperparameters

Davide Anguita, Luca Oneto, Sandro Ridella, 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

Long-term energy load forecasting using Auto-Regressive and approximating Support Vector Regression

D. Anguita, A. Ghio, L. Ghelardoni, 2012, 2012 IEEE International Energy Conference and Exhibition (ENERGYCON).

Crack random forest for arbitrary large datasets

Davide Anguita, Luca Oneto, Alessandro Lulli, 2017, 2017 IEEE International Conference on Big Data (Big Data).

ReForeSt: Random Forests in Apache Spark

Davide Anguita, Luca Oneto, Alessandro Lulli, 2017, ICANN.

Unsupervised clustering and the capacity of support vector machines

Davide Anguita, Sandro Ridella, Rodolfo Zunino, 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).