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
Van Dooren Paul
  • ESANN 2015 - Rank-constrained optimization: a Riemannian manifold approach [Details]
Adrien Pavao
  • ESANN 2021 - Judging competitions and benchmarks: a candidate election approach [Details]
  • ESANN 2022 - Filtering participants improves generalization in competitions and benchmarks [Details]
Adrien Pavao
  • ESANN 2019 - Privacy Preserving Synthetic Health Data [Details]
D. Pavisic
  • ESANN 1995 - Active noise control with dynamic recurrent neural networks [Details]
  • ESANN 1995 - Identification of the human arm kinetics using dynamic recurrent neural networks [Details]
  • ESANN 1996 - Adaptative time constants improve the dynamic features of recurrent neural networks [Details]
  • ESANN 1996 - Negative initial weights improve learning in recurrent neural networks [Details]
  • ESANN 1997 - Evidence of efficiency of recurrent neural networks with ARMA-like units [Details]
Klaus R. Pawelzik
  • ESANN 2006 - On-line adaptation of neuro-prostheses with neuronal evaluation signals [Details]
K. Pawelzik
  • ESANN 1999 - Hidden Markov gating for prediction of change points in switching dynamical systems [Details]
Aurore Payen
  • ESANN 2015 - Efficient unsupervised clustering for spatial birds population analysis along the river Loire [Details]
  • ESANN 2015 - NLDR methods for high dimensional NIRS dataset : application to vineyard soils characterization [Details]
M. Payeras
  • ESANN 1998 - A new dynamic LVQ-based classifier and its application to handwritten character recognition [Details]
J.A. Pecas Lopes
  • ESANN 2000 - Load forecasting dealing with medium voltage network reconfiguration [Details]
G. Pechanek
  • ESANN 1995 - XOR and backpropagation learning: in and out of the chaos? [Details]
Jonathan Peck
  • ESANN 2019 - Detecting adversarial examples with inductive Venn-ABERS predictors [Details]
Jean Pierre Pecuchet
  • ESANN 2008 - Automatic alignment of medical vs. general terminologies [Details]
Joseph Pedersen
  • ESANN 2021 - Quantifying Resemblance of Synthetic Medical Time-Series [Details]
Carlos Pedreira
  • ESANN 2014 - Credit analysis with a clustering RAM-based neural classifier [Details]
C. E. Pedreira
  • ESANN 2003 - Mixture of Experts and Local-Global Neural Networks [Details]
D. I. Pedreira Iparraguirre
  • ESANN 2000 - Chaotic time series prediction using the Kohonen algorithm [Details]
Luca Pedrelli
  • ESANN 2018 - Deep Echo State Networks for Diagnosis of Parkinson's Disease [Details]
  • ESANN 2019 - Comparison between DeepESNs and gated RNNs on multivariate time-series prediction [Details]
Luca Pedrelli
  • ESANN 2021 - Deep Echo State Networks for Functional Ambulation Categories Estimation [Details]
Nuno Miguel Pedrosa de Barros
  • ESANN 2016 - A machine learning pipeline for supporting differentiation of glioblastomas from single brain metastases [Details]
Witold Pedrycz
  • ESANN 2009 - Lukasiewicz fuzzy logic networks and their ultra low power hardware implementation [Details]
  • ESANN 2010 - Programmable triangular neighborhood functions of Kohonen Self-Organizing Maps realized in CMOS technology [Details]
  • ESANN 2011 - Fisherman learning algorithm of the SOM realized in the CMOS technology [Details]
  • ESANN 2012 - Implementation Issues of Kohonen Self-Organizing Map Realized on FPGA [Details]
  • ESANN 2012 - Low-Power Manhattan Distance Calculation Circuit for Self-Organizing Neural Networks Implemented in the CMOS Technology [Details]
W. Pedrycz
  • ESANN 2001 - One-to-many mappings represented on feed-forward networks [Details]
Maciej Pedzisz
  • ESANN 2006 - A simple idea to separate convolutive mixtures in an undetermined scenario [Details]
Miguel Pego Roque
  • ESANN 2024 - Deep Temporal Consensus Clustering for Patient Stratification in Amyotrophic Lateral Sclerosis [Details]
Pei Ling Lai
  • ESANN 1998 - Canonical correlation analysis using artificial neural networks [Details]
Enrique Pelayo
  • ESANN 2011 - SO-VAT: Self-Organizing Visual Assessment of cluster Tendency for large data sets [Details]
  • ESANN 2012 - magnitude sensitive competitive learning [Details]
Kristiaan Pelckmans
  • ESANN 2004 - sparse LS-SVMs using additive regularization with a penalized validation criterion [Details]
  • ESANN 2007 - Convex optimization for the design of learning machines [Details]
  • ESANN 2008 - Survival SVM: a practical scalable algorithm [Details]
  • ESANN 2009 - Transductively Learning from Positive Examples Only [Details]
  • ESANN 2010 - On the use of a clinical kernel in survival analysis [Details]
Joris Pelemans
  • ESANN 2020 - On the long-term learning ability of LSTM LMs [Details]
Reynier Peletier
  • ESANN 2018 - Globular Cluster Detection in the Gaia Survey [Details]
Reynier Peletier
  • ESANN 2023 - Improved the locally aligned ant technique (LAAT) strategy to recover manifolds embedded in strong noise [Details]
  • ESANN 2025 - Adaptive Locally Aligned Ant Technique for Manifold Detection and Denoising [Details]
M. Pelillo
  • ESANN 1995 - An asymmetric associative memory model based on relaxation labeling processes [Details]