Steven Van Vaerenbergh

According to our database, Steven Van Vaerenbergh authored at least 46 papers between 2006 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

A Data Mining Approach for Health Transport Demand.

Jorge Blanco Prieto, Marina Ferreras González, Steven Van Vaerenbergh, Oscar Jesús Cosido Cobos

Mach. Learn. Knowl. Extr., March, 2024

Learning About Student Performance From Moodle Logs in a Higher Education Context With Gaussian Processes.

Adrián Pérez-Suay, Valero Laparra, Steven Van Vaerenbergh, Ana Belen Pascual Venteo

Rev. Iberoam. de Tecnol. del Aprendiz., 2024

Real-Time Machine Learning Strategies for a New Kind of Neuroscience Experiments.

Ayesha Vermani, Matthew Dowling, Hyungju Jeon, Ian D. Jordan, Josue Nassar, Yves Bernaerts, Yuan Zhao, Steven Van Vaerenbergh, Il Memming Park

Proceedings of the 32nd European Signal Processing Conference, 2024

2023

Data-Driven Modeling Through the Moodle Learning Management System: An Empirical Study Based on a Mathematics Teaching Subject.

Adrián Pérez-Suay, Steven Van Vaerenbergh, Pascual D. Diago, Ana Belen Pascual Venteo, Francesc J. Ferri

Rev. Iberoam. de Tecnol. del Aprendiz., February, 2023

2021

A Classification of Artificial Intelligence Systems for Mathematics Education.

Steven Van Vaerenbergh, Adrián Pérez-Suay

CoRR, 2021

2020

Multi-Channel Factor Analysis With Common and Unique Factors.

David Ramírez, Ignacio Santamaría, Louis L. Scharf, Steven Van Vaerenbergh

IEEE Trans. Signal Process., 2020

Complex-Valued Neural Networks With Nonparametric Activation Functions.

Simone Scardapane, Steven Van Vaerenbergh, Amir Hussain, Aurelio Uncini

IEEE Trans. Emerg. Top. Comput. Intell., 2020

Power prediction for electric vehicles using online machine learning.

Stephan Rhode, Steven Van Vaerenbergh, Matthias Pfriem

Eng. Appl. Artif. Intell., 2020

2019

Kafnets: Kernel-based non-parametric activation functions for neural networks.

Simone Scardapane, Steven Van Vaerenbergh, Simone Totaro, Aurelio Uncini

Neural Networks, 2019

On the Stability and Generalization of Learning with Kernel Activation Functions.

Michele Cirillo, Simone Scardapane, Steven Van Vaerenbergh, Aurelio Uncini

CoRR, 2019

Widely Linear Kernels for Complex-valued Kernel Activation Functions.

Simone Scardapane, Steven Van Vaerenbergh, Danilo Comminiello, Aurelio Uncini

Proceedings of the IEEE International Conference on Acoustics, 2019

2018

Complex-valued Neural Networks with Non-parametric Activation Functions.

Simone Scardapane, Steven Van Vaerenbergh, Amir Hussain, Aurelio Uncini

CoRR, 2018

Recurrent Neural Networks with flexible Gates using Kernel activation Functions.

Simone Scardapane, Steven Van Vaerenbergh, Danilo Comminiello, Simone Totaro, Aurelio Uncini

Proceedings of the 28th IEEE International Workshop on Machine Learning for Signal Processing, 2018

Pattern Localization in Time Series Through Signal-To-Model Alignment in Latent Space.

Steven Van Vaerenbergh, Ignacio Santamaría, Victor Elvira, Matteo Salvatori

Proceedings of the 2018 IEEE International Conference on Acoustics, 2018

Improving Graph Convolutional Networks with Non-Parametric Activation Functions.

Simone Scardapane, Steven Van Vaerenbergh, Danilo Comminiello, Aurelio Uncini

Proceedings of the 26th European Signal Processing Conference, 2018

Analysis and Classification of MoCap Data by Hilbert Space Embedding-Based Distance and Multikernel Learning.

Juan Diego Pulgarin-Giraldo, Andrés Marino Álvarez-Meza, Steven Van Vaerenbergh, Ignacio Santamaría, Germán Castellanos-Domínguez

Proceedings of the Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 2018

An alternating optimization algorithm for two-channel factor analysis with common and uncommon factors.

David Ramírez, Ignacio Santamaría, Steven Van Vaerenbergh, Louis L. Scharf

Proceedings of the 52nd Asilomar Conference on Signals, Systems, and Computers, 2018

2017

Kernel canonical correlation analysis for robust cooperative spectrum sensing in cognitive radio networks.

Julio Manco-Vásquez, Steven Van Vaerenbergh, Javier Vía Rodríguez, Ignacio Santamaría

Trans. Emerg. Telecommun. Technol., 2017

Kafnets: kernel-based non-parametric activation functions for neural networks.

Simone Scardapane, Steven Van Vaerenbergh, Aurelio Uncini

CoRR, 2017

Recursive multikernel filters exploiting nonlinear temporal structure.

Steven Van Vaerenbergh, Simone Scardapane, Ignacio Santamaría

Proceedings of the 25th European Signal Processing Conference, 2017

2016

On the relationship between online Gaussian process regression and kernel least mean squares algorithms.

Steven Van Vaerenbergh, Jesus Fernandez-Bes, Victor Elvira

Proceedings of the 26th IEEE International Workshop on Machine Learning for Signal Processing, 2016

A split kernel adaptive filtering architecture for nonlinear acoustic echo cancellation.

Steven Van Vaerenbergh, Luis Antonio Azpicueta-Ruiz, Danilo Comminiello

Proceedings of the 24th European Signal Processing Conference, 2016

2015

A probabilistic least-mean-squares filter.

Jesus Fernandez-Bes, Víctor Elvira, Steven Van Vaerenbergh

Proceedings of the 2015 IEEE International Conference on Acoustics, 2015

2014

A Gaussian Process Model for Data Association and a Semidefinite Programming Solution.

Miguel Lázaro-Gredilla, Steven Van Vaerenbergh

IEEE Trans. Neural Networks Learn. Syst., 2014

Experimental evaluation of a cooperative kernel-based approach for robust spectrum sensing.

Julio Manco-Vásquez, Steven Van Vaerenbergh, Javier Vía, Ignacio Santamaría

Proceedings of the IEEE 8th Sensor Array and Multichannel Signal Processing Workshop, 2014

Physical layer authentication based on channel response tracking using Gaussian processes.

Steven Van Vaerenbergh, Oscar Gonzalez, Javier Vía, Ignacio Santamaría

Proceedings of the IEEE International Conference on Acoustics, 2014

Kernel-based identification of Hammerstein systems for nonlinear acoustic echo-cancellation.

Steven Van Vaerenbergh, Luis Antonio Azpicueta-Ruiz

Proceedings of the IEEE International Conference on Acoustics, 2014

Probabilistic kernel least mean squares algorithms.

Il Memming Park, Sohan Seth, Steven Van Vaerenbergh

Proceedings of the IEEE International Conference on Acoustics, 2014

2013

Blind Identification of SIMO Wiener Systems Based on Kernel Canonical Correlation Analysis.

Steven Van Vaerenbergh, Javier Vía, Ignacio Santamaría

IEEE Trans. Signal Process., 2013

Gaussian Processes for Nonlinear Signal Processing: An Overview of Recent Advances.

Fernando Pérez-Cruz, Steven Van Vaerenbergh, Juan José Murillo-Fuentes, Miguel Lázaro-Gredilla, Ignacio Santamaría

IEEE Signal Process. Mag., 2013

Semi-supervised object recognition based on Connected Image Transformations.

Steven Van Vaerenbergh, Ignacio Santamaría, Paolo Emilio Barbano

Expert Syst. Appl., 2013

Gaussian Processes for Nonlinear Signal Processing

Fernando Pérez-Cruz, Steven Van Vaerenbergh, Juan José Murillo-Fuentes, Miguel Lázaro-Gredilla, Ignacio Santamaría

CoRR, 2013

Bayesian Extensions of Kernel Least Mean Squares.

Il Memming Park, Sohan Seth, Steven Van Vaerenbergh

CoRR, 2013

Adaptive kernel canonical correlation analysis algorithms for maximum and minimum variance.

Steven Van Vaerenbergh, Javier Vía, Julio Manco-Vásquez, Ignacio Santamaría

Proceedings of the IEEE International Conference on Acoustics, 2013

2012

Kernel Recursive Least-Squares Tracker for Time-Varying Regression.

Steven Van Vaerenbergh, Miguel Lázaro-Gredilla, Ignacio Santamaría

IEEE Trans. Neural Networks Learn. Syst., 2012

Overlapping Mixtures of Gaussian Processes for the data association problem.

Miguel Lázaro-Gredilla, Steven Van Vaerenbergh, Neil D. Lawrence

Pattern Recognit., 2012

Estimation of the forgetting factor in kernel recursive least squares.

Steven Van Vaerenbergh, Ignacio Santamaría, Miguel Lázaro-Gredilla

Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing, 2012

2011

A Bayesian approach to tracking with kernel recursive least-squares.

Miguel Lázaro-Gredilla, Steven Van Vaerenbergh, Ignacio Santamaría

Proceedings of the 2011 IEEE International Workshop on Machine Learning for Signal Processing, 2011

Semi-supervised handwritten digit recognition using very few labeled data.

Steven Van Vaerenbergh, Ignacio Santamaría, Paolo Emilio Barbano

Proceedings of the IEEE International Conference on Acoustics, 2011

2010

Fixed-budget kernel recursive least-squares.

Steven Van Vaerenbergh, Ignacio Santamaría, Weifeng Liu, José C. Príncipe

Proceedings of the IEEE International Conference on Acoustics, 2010

2008

Adaptive Kernel Canonical Correlation Analysis Algorithms for Nonparametric Identification of Wiener and Hammerstein Systems.

Steven Van Vaerenbergh, Javier Vía, Ignacio Santamaría

EURASIP J. Adv. Signal Process., 2008

2007

Nonlinear System Identification using a New Sliding-Window Kernel RLS Algorithm.

Steven Van Vaerenbergh, Javier Vía, Ignacio Santamaría

J. Commun., 2007

A spectral clustering algorithm for decoding fast time-varying BPSK mimo channels.

Steven Van Vaerenbergh, Emilio J. Estébanez, Ignacio Santamaría

Proceedings of the 15th European Signal Processing Conference, 2007

2006

A spectral clustering approach to underdetermined postnonlinear blind source separation of sparse sources.

Steven Van Vaerenbergh, Ignacio Santamaría

IEEE Trans. Neural Networks, 2006

Online Kernel Canonical Correlation Analysis for Supervised Equalization of Wiener Systems.

Steven Van Vaerenbergh, Javier Vía, Ignacio Santamaría

Proceedings of the International Joint Conference on Neural Networks, 2006

A Sliding-Window Kernel RLS Algorithm and Its Application to Nonlinear Channel Identification.

Steven Van Vaerenbergh, Javier Vía, Ignacio Santamaría

Proceedings of the 2006 IEEE International Conference on Acoustics Speech and Signal Processing, 2006