Electronic proceedings author index

A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z
Haroldo Velho
  • ESANN 2019 - Weightless neural systems for deforestation surveillance and image-based navigation of UAVs in the Amazon forest [Details]
Petar Velickovic
  • ESANN 2018 - Multi-omics data integration using cross-modal neural networks [Details]
Riza Velioglu
  • ESANN 2026 - But Are These Images Conceptually Similar? [Details]
Marley Vellasco
  • ESANN 2004 - Data Mining Techniques on the Evaluation of Wireless Churn [Details]
Alfredo Vellido
  • ESANN 2005 - Functional topographic mapping for robust handling of outliers in brain tumour data [Details]
  • ESANN 2005 - Handling outliers and missing data in brain tumour clinical assessment using t-GTM [Details]
  • ESANN 2006 - Learning what is important: feature selection and rule extraction in a virtual course [Details]
  • ESANN 2007 - Identification of churn routes in the Brazilian telecommunications market [Details]
  • ESANN 2008 - DSS-oriented exploration of a multi-centre magnetic resonance spectroscopy brain tumour dataset through visualization [Details]
  • ESANN 2008 - Machine learning in cancer research: implications for personalised medicine [Details]
  • ESANN 2010 - Computational Intelligence in biomedicine: Some contributions [Details]
  • ESANN 2010 - Kernel generative topographic mapping [Details]
  • ESANN 2010 - Segmentation of EMG time series using a variational Bayesian approach for the robust estimation of cortical silent periods [Details]
  • ESANN 2010 - Spectral Prototype Extraction for dimensionality reduction in brain tumour diagnosis [Details]
  • ESANN 2011 - A probabilistic approach to the visual exploration of G Protein-Coupled Receptor sequences [Details]
  • ESANN 2011 - Seeing is believing: The importance of visualization in real-world machine learning applications [Details]
  • ESANN 2012 - Cartogram representation of the batch-SOM magnification factor [Details]
  • ESANN 2012 - Making machine learning models interpretable [Details]
  • ESANN 2013 - A quotient basis kernel for the prediction of mortality in severe sepsis patients [Details]
  • ESANN 2013 - Robust cartogram visualization of outliers in manifold learning [Details]
  • ESANN 2013 - Visualizing pay-per-view television customers churn using cartograms and flow maps [Details]
  • ESANN 2016 - A machine learning pipeline for supporting differentiation of glioblastomas from single brain metastases [Details]
  • ESANN 2016 - Bayesian semi non-negative matrix factorisation [Details]
  • ESANN 2016 - Physics and Machine Learning: Emerging Paradigms [Details]
  • ESANN 2018 - Bioinformatics and medicine in the era of deep learning [Details]
  • ESANN 2019 - Societal Issues in Machine Learning: When Learning from Data is Not Enough [Details]
Alfredo Vellido
  • ESANN 2021 - The Coming of Age of Interpretable and Explainable Machine Learning Models [Details]
  • ESANN 2025 - Altered emotion recognition from psychiatric patient profiles using Machine Learning [Details]
  • ESANN 2025 - Machine Learning and applied Artificial Intelligence in cognitive sciences and pyschology: a tutorial [Details]
Alfredo Vellido Alcacena
  • ESANN 2014 - Misclassification of class C G-protein-coupled receptors as a label noise problem [Details]
W. Velthuis
  • ESANN 1998 - Parsimonious learning feed-forward control [Details]
Sébastien VELUT
  • ESANN 2024 - Deep Riemannian Neural Architectures for Domain Adaptation in Burst cVEP-based Brain Computer Interface [Details]
G. Kumar Venayagamoorthy
  • ESANN 2026 - Techniques for Reliable, Safe and Robust AI Applications [Details]
R.S. Venema
  • ESANN 1996 - Time series prediction using neural networks and its application to artificial human walking [Details]
Jarkko Venna
  • ESANN 2006 - Visualizing gene interaction graphs with local multidimensional scaling [Details]
Joost Vennekens
  • ESANN 2025 - Enhancing Computer Vision with Knowledge: a Rummikub Case Study [Details]
S. Ventura Soto
  • ESANN 2001 - A two steps method: non linear regression and pruning neural network for analyzing multicomponent mixtures [Details]
Gilles Venturini
  • ESANN 2013 - Delaunay simplices pruning based clustering [Details]
  • ESANN 2016 - Incremental hierarchical indexing and visualisation of large image collections [Details]
Jelle Veraart
  • ESANN 2016 - Initializing nonnegative matrix factorization using the successive projection algorithm for multi-parametric medical image segmentation [Details]
J.J. Verbeek
  • ESANN 2002 - Fast nonlinear dimensionality reduction with topology preserving networks [Details]
  • ESANN 2003 - Self-Organization by Optimizing Free-Energy [Details]
Verbockhaven Verbockhaven
  • ESANN 2025 - Growth strategies for arbitrary DAG neural architectures [Details]
H.B. Verbruggen
  • ESANN 1996 - Regulated Activation Weights Neural Network (RAWN) [Details]
Laurent Vercueil
  • ESANN 2009 - Sparse differential connectivity graph of scalp EEG for epileptic patients [Details]
Pablo F. Verdes
  • ESANN 2007 - Computational Intelligence approaches to causality detection [Details]
Galina Veres
  • ESANN 2013 - Automated operational states detection for drilling systems control in critical conditions [Details]
  • ESANN 2015 - Data Analytics for Drilling Operational States Classifications [Details]
Marian Verhelst
  • ESANN 2014 - Context- and cost-aware feature selection in ultra-low-power sensor interfaces [Details]
  • ESANN 2018 - Feature noise tuning for resource efficient Bayesian Network Classifiers [Details]
Théo Verhelst
  • ESANN 2026 - Identifying counterfactual probabilities using bivariate distributions and uplift modeling [Details]
Tanmay Verlekar
  • ESANN 2025 - Shallow convolution and attention-based models for micro-expression recognition [Details]
Michel Verleysen
  • ESANN 2004 - functional radial basis function networks [Details]
Michel Verleysen
  • ESANN 2004 - How to project `circular' manifolds using geodesic distances? [Details]
Michel Verleysen
  • ESANN 2004 - fast bootstrap for least-square support vector machines [Details]
Michel Verleysen
  • ESANN 2004 - Flexible and Robust Bayesian Classification by Finite Mixture Models [Details]
Michel Verleysen
  • ESANN 2020 - Perplexity-free Parametric t-SNE [Details]
  • ESANN 2021 - Impact of data subsamplings in Fast Multi-Scale Neighbor Embedding. [Details]
  • ESANN 2021 - Stochastic quartet approach for fast multidimensional scaling [Details]
  • ESANN 2023 - On the number of latent representations in deep neural networks for tabular data [Details]
  • ESANN 2024 - Forget early exaggeration in t-SNE: early hierarchization preserves global structure [Details]
  • ESANN 2025 - Can MDS rival with t-SNE by using the symmetric Kullback-Leibler divergence\\ across neighborhoods as a pseudo-distance? [Details]
  • ESANN 2026 - Interpretable Parametric Neighbour Embedding [Details]
  • ESANN 2026 - Multi-Scale Stochastic Neighbor Embedding with Twice Adaptive Bandwidths [Details]