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
Jesse De Does
  • ESANN 2015 - Combining higher-order N-grams and intelligent sample selection to improve language modeling for Handwritten Text Recognition [Details]
Radu Dogaru
  • ESANN 2020 - Improving Light-weight Convolutional Neural Networks for Face Recognition Targeting Resource Constrained Platforms [Details]
R. Dogaru
  • ESANN 1996 - Fast signal recognition and detection using ART1 neural networks and nonlinear preprocessing units based on time delay embeddings [Details]
Narjes Doggaz
  • ESANN 2025 - Proactive Privacy Risk Assessment for Android Applications: A Machine Learning Based-Approach [Details]
Kevin Doherty
  • ESANN 2004 - Non-Euclidean norms and data normalisation [Details]
  • ESANN 2005 - TreeGNG - hierarchical topological clustering [Details]
  • ESANN 2006 - Topological Correlation [Details]
Patrick Doherty
  • ESANN 2019 - Deep RL for autonomous robots: limitations and safety challenges [Details]
Klaus Dohmen
  • ESANN 2025 - Learning of Probability Estimates for System and Network Reliability Analysis by Means of Matrix Learning Vector Quantization [Details]
  • ESANN 2025 - Towards Learning Vector Quantization in the Setting of Homomorphic Encryption [Details]
  • ESANN 2026 - Domination Reliability Analysis Based on Graph Features Using Generalized Matrix LVQ [Details]
Vladislav Dolganov
  • ESANN 2015 - PCA-based algorithm for feature score measures ensemble construction [Details]
Sergey Dolgobrodov
  • ESANN 2005 - An artificial neural network for analysing the survival of patients with colorectal cancer [Details]
Kristin Domaschke
  • ESANN 2014 - Utilization of Chemical Structure Information for Analysis of Spectra Composites [Details]
  • ESANN 2015 - Learning matrix quantization and variants of relevance learning [Details]
Inês Domingues
  • ESANN 2018 - Interpreting deep learning models for ordinal problems [Details]
Enrique Domínguez
  • ESANN 2010 - An ART-type network approach for video object detection [Details]
  • ESANN 2010 - Web Document Clustering based on a Hierarchical Self-Organizing Model [Details]
Manuel Domínguez
  • ESANN 2017 - Latent variable analysis in hospital electric power demand using non-negative matrix factorization [Details]
E. Domínguez Merino
  • ESANN 2003 - Neural Network Algorithms for the p-Median Problem [Details]
R. Dominguez-Castro
  • ESANN 2001 - CMOS design of focal plane programmable array processors [Details]
C. Domnisoru
  • ESANN 2002 - Double self-organizing maps to cluster gene expression data [Details]
  • ESANN 2002 - Improving robustness of fuzzy gene modeling [Details]
Xavier Domont
  • ESANN 2007 - A hierarchical model for syllable recognition [Details]
N. Donckers
  • ESANN 1999 - Extraction of intrinsic dimension using CCA - Application to blind sources separation [Details]
  • ESANN 2000 - A robust non-linear projection method [Details]
Nicolas Donckers
  • ESANN 2004 - HMM and IOHMM modeling of EEG rhythms for asynchronous BCI systems [Details]
D.J. Done
  • ESANN 2002 - Connectionist models investigating representations formed in the sequential generation of characters [Details]
Shuyu Dong
  • ESANN 2017 - Learning sparse models of diffusive graph signals [Details]
  • ESANN 2019 - Preconditioned conjugate gradient algorithms for graph regularized matrix completion [Details]
Michele Donini
  • ESANN 2014 - Easy multiple kernel learning [Details]
  • ESANN 2015 - Feature and kernel learning [Details]
  • ESANN 2016 - Advances in Learning with Kernels: Theory and Practice in a World of growing Constraints [Details]
  • ESANN 2016 - Measuring the Expressivity of Graph Kernels through the Rademacher Complexity [Details]
  • ESANN 2017 - Fast hyperparameter selection for graph kernels via subsampling and multiple kernel learning [Details]
  • ESANN 2017 - Learning dot-product polynomials for multiclass problems [Details]
  • ESANN 2018 - Emerging trends in machine learning: beyond conventional methods and data [Details]
  • ESANN 2019 - PAC-Bayes and Fairness: Risk and Fairness Bounds on Distribution Dependent Fair Priors [Details]
Michele Donini
  • ESANN 2020 - Learning Deep Fair Graph Neural Networks [Details]
Benjamin Donnot
  • ESANN 2018 - Fast Power system security analysis with Guided Dropout [Details]
  • ESANN 2019 - LEAP nets for power grid perturbations [Details]
D. Donoho
  • ESANN 2002 - When does geodesic distance recover the true hidden parametrization of families of articulated images? [Details]
Balthazar Donon
  • ESANN 2019 - LEAP nets for power grid perturbations [Details]
Alejandro Dopico-Castro
  • ESANN 2026 - FedHENet: A Frugal Federated Learning Framework for Heterogeneous Environments [Details]
Gauthier Doquire
  • ESANN 2011 - Mutual information based feature selection for mixed data [Details]
  • ESANN 2011 - Mutual information for feature selection with missing data [Details]
  • ESANN 2012 - On the Potential Inadequacy of Mutual Information for Feature Selection [Details]
  • ESANN 2013 - Risk Estimation and Feature Selection [Details]
B. Dorizzi
  • ESANN 1993 - MLP modular networks for multi-class recognition [Details]
S. R. Dorling
  • ESANN 2003 - Statistical downscaling with artificial neural networks [Details]