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
A. Lendasse
  • ESANN 1998 - Forecasting time-series by Kohonen classification [Details]
  • ESANN 1999 - Extraction of intrinsic dimension using CCA - Application to blind sources separation [Details]
  • ESANN 2000 - A robust non-linear projection method [Details]
  • ESANN 2000 - Time series forecasting using CCA and Kohonen maps - application to electricity consumption [Details]
  • ESANN 2001 - Input data reduction for the prediction of financial time series [Details]
  • ESANN 2002 - Curvilinear Distance Analysis versus Isomap [Details]
  • ESANN 2002 - Width optimization of the Gaussian kernels in Radial Basis Function Networks [Details]
  • ESANN 2003 - Fast approximation of the bootstrap for model selection [Details]
Amaury Lendasse
  • ESANN 2021 - A Multi-ELM Model for Incomplete Data [Details]
  • ESANN 2021 - Machine Learning for Measuring and Analyzing Online Social Communications [Details]
  • ESANN 2021 - NNBMSS: a Novel and Fast Method for Model Structure Selection [Details]
  • ESANN 2024 - A Two-Stage Approach for Implicit Bias Detection in Generative Language Models [Details]
Amaury Lendasse
  • ESANN 2004 - fast bootstrap for least-square support vector machines [Details]
  • ESANN 2005 - Mutual information and gamma test for input selection [Details]
  • ESANN 2005 - Pruned lazy learning models for time series prediction [Details]
  • ESANN 2006 - Determination of the Mahalanobis matrix using nonparametric noise estimations [Details]
  • ESANN 2006 - EM-algorithm for training of state-space models with application to time series prediction [Details]
  • ESANN 2006 - LS-SVM functional network for time series prediction [Details]
  • ESANN 2006 - Time series prediction using DirRec strategy [Details]
  • ESANN 2007 - Nearest Neighbor Distributions and Noise Variance Estimation [Details]
  • ESANN 2007 - SOM+EOF for finding missing values [Details]
  • ESANN 2008 - A Methodology for Building Regression Models using Extreme Learning Machine: OP-ELM [Details]
  • ESANN 2008 - Linear Projection based on Noise Variance Estimation - Application to Spectral Data [Details]
  • ESANN 2008 - Using the Delta Test for Variable Selection [Details]
  • ESANN 2009 - A faster model selection criterion for OP-ELM and OP-KNN: Hannan-Quinn criterion [Details]
  • ESANN 2009 - Applying Mutual Information for Prototype or Instance Selection in Regression Problems [Details]
  • ESANN 2009 - X-SOM and L-SOM: a nested approach for missing value imputation [Details]
  • ESANN 2010 - Ensemble Modeling with a Constrained Linear System of Leave-One-Out Outputs [Details]
  • ESANN 2010 - Machine Learning Techniques based on Random Projections [Details]
  • ESANN 2010 - Solving Large Regression Problems using an Ensemble of GPU-accelerated ELMs [Details]
  • ESANN 2011 - Adaptive Kernel Smoothing Regression for Spatio-Temporal Environmental Datasets [Details]
  • ESANN 2011 - Locating Anomalies Using Bayesian Factorizations and Masks [Details]
  • ESANN 2012 - Relevance learning for time series inspection [Details]
  • ESANN 2013 - Forecasting Financial Markets with Classified Tactical Signals [Details]
  • ESANN 2013 - Visualizing dependencies of spectral features using mutual information [Details]
  • ESANN 2014 - Finding Originally Mislabels with MD-ELM [Details]
  • ESANN 2015 - Towards a Tomographic Index of Systemic Risk Measures [Details]
  • ESANN 2017 - Advanced query strategies for Active Learning with Extreme Learning Machines [Details]
Amaury Lendasse
  • ESANN 2026 - The Alignment Gate: Intent and Instruction Guardrails for Agentic AI [Details]
R. Lengellé
  • ESANN 1995 - Performance analysis of a MLP weight initialization algorithm [Details]
Hendrik Lensch
  • ESANN 2020 - A Distributed Neural Network Architecture for Robust Non-Linear Spatio-Temporal Prediction [Details]
Aurélia Léon
  • ESANN 2016 - Policy-gradient methods for Decision Trees [Details]
Franco Leonardo
  • ESANN 2005 - Artificial neural networks and prognosis in medicine. Survival analysis in breast cancer patients [Details]
Fabrizio Leopardi
  • ESANN 2026 - Interpreting Logical Explanations of Classifying Neural Networks [Details]
Alessandro Leparulo
  • ESANN 2023 - Real-time Detection of Evoked Potentials by Deep Learning: a Case Study [Details]
Philippe Leray
  • ESANN 2010 - Towards sub-quadratic learning of probability density models in the form of mixtures of trees [Details]
  • ESANN 2015 - On the equivalence between regularized NMF and similarity-augmented graph partitioning [Details]
Boaz Lerner
  • ESANN 2017 - Learning human behaviors and lifestyle by capturing temporal relations in mobility patterns [Details]
  • ESANN 2018 - Temporal modeling of ALS using longitudinal data and long-short term memory-based algorithm [Details]
E. Lerouge
  • ESANN 1999 - A hybrid system for fraud detection in mobile communications [Details]
Sam Leroux
  • ESANN 2026 - THDC: Training Hyperdimensional Computing Models with Backpropagation [Details]
Sylvain Lesage
  • ESANN 2006 - A survey of Sparse Component Analysis for blind source separation: principles, perspectives, and new challenges [Details]
Markus Leskinen
  • ESANN 2017 - Prediction of preterm infant mortality with Gaussian process classification [Details]
Timothée Lesort
  • No papers found
Timothée Lesort
  • ESANN 2022 - Tutorial - Continual Learning beyond classification [Details]
  • ESANN 2024 - Continual Learning of Deep Neural Networks in The Age of Big Data [Details]
Sylvain Lespinats
  • No papers found
Sylvain Lespinats
  • ESANN 2010 - Mapping without visualizing local default is nonsense [Details]
Sylvain Lespinats
  • ESANN 2022 - Supervised dimensionality reduction technique accounting for soft classes [Details]
Simon Leszek
  • ESANN 2026 - A comparison of open time-series foundation models for industrial manufacturing applications [Details]
  • ESANN 2026 - Efficient and Resilient Machine Learning for Industrial Applications [Details]
P. Letremy
  • ESANN 2002 - Advantages and drawbacks of the Batch Kohonen algorithm [Details]
  • ESANN 2003 - Analyzing surveys using the Kohonen algorithm [Details]
Michael Leuenberger
  • ESANN 2014 - Feature selection in environmental data mining combining Simulated Annealing and Extreme Learning Machine [Details]
  • ESANN 2015 - Morisita-based feature selection for regression problems [Details]
C.T. Leung
  • ESANN 1995 - Neural network piecewise linear preprocessing for time-series prediction [Details]
Maciej Lewenstein
  • ESANN 2020 - Understanding and improving unsupervised training of Boltzman machines [Details]
milad leyli abadi
  • ESANN 2021 - Deep Neural Networks for Classification of Riding Patterns: with a focus on explainability [Details]
  • ESANN 2024 - Evaluation methodology for disentangled uncertainty quantification on regression models [Details]
Peng Li
  • ESANN 2009 - A Variational Approach to Semi-Supervised Clustering [Details]
Shanglin Li
  • ESANN 2024 - Geometric Deep Learning to Enhance Imbalanced Domain Adaptation in EEG [Details]
Bo Li
  • ESANN 2019 - Modal sense classification with task-specific context embeddings [Details]