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
Alceu de Souza Britto Jr.
  • ESANN 2022 - A Machine Learning Approach for School Dropout Prediction in Brazil [Details]
Alceu de Souza Britto Jr.
  • No papers found
Stefano Bromuri
  • ESANN 2023 - Graph-based Categorical Embedding [Details]
  • ESANN 2023 - Language Modeling in Logistics: Customer Calling Prediction [Details]
Chris Bronk
  • ESANN 2021 - Machine Learning for Measuring and Analyzing Online Social Communications [Details]
Johanna Bronner
  • ESANN 2021 - Differentially Private Time Series Generation [Details]
Mats Brorsson
  • ESANN 2019 - time series modelling of market price in real-time bidding [Details]
Maximilian Broß
  • ESANN 2023 - Segmentation and Analysis of Lumbar Spine MRI Scans for Vertebral Body Measurements [Details]
R. K. Brouwer
  • ESANN 1999 - An integer recurrent artificial neural network for classifying feature vectors [Details]
  • ESANN 2001 - One-to-many mappings represented on feed-forward networks [Details]
R.K. Brouwer
  • ESANN 2002 - Storing many-to-many mappings on a feed-forward neural network using fuzzy sets [Details]
D. Brown
  • ESANN 1998 - Output jitter diverges to infinity, converges to zero or remains constant [Details]
  • ESANN 1998 - What is observable in a class of neurodynamics? [Details]
Gavin Brown
  • ESANN 2017 - Degrees of Freedom in Regression Ensembles [Details]
  • ESANN 2017 - Mutual information for improving the efficiency of the SCH algorithm [Details]
Marc Brown
  • ESANN 2010 - The Application of Gaussian Processes in the Prediction of Percutaneous Absorption for Mammalian and Synthetic Membranes [Details]
A. Browne
  • ESANN 1994 - Variable binding in a neural network using a distributed representation [Details]
  • No papers found
Matthias Brucklacher
  • ESANN 2021 - Hierarchical Planning in Multilayered State-Action Networks [Details]
Dominik Brugger
  • ESANN 2008 - Direct and inverse solution for a stimulus adaptation problem using SVR [Details]
Joan Bruna Estrach
  • ESANN 2010 - Geometric models with co-occurrence groups [Details]
N. Brunel
  • ESANN 1997 - Optimal tuning curves for neurons spiking as a Poisson process [Details]
Camille Brunet
  • ESANN 2009 - Classification of high-dimensional data for cervical cancer detection [Details]
  • ESANN 2011 - Probabilistic Fisher discriminant analysis [Details]
Joao Victor Bruneti Severino
  • ESANN 2018 - Radar Based Pedestrian Detection using Support Vector Machine and the Micro Doppler Effect [Details]
Cornelia Brüß
  • ESANN 2006 - Fuzzy image segmentation with Fuzzy Labelled Neural Gas [Details]
  • ESANN 2007 - Visualization of Fuzzy Information in Fuzzy-Classification for Image Segmentation using MDS [Details]
J. Bruske
  • ESANN 1996 - Praticing Q-learning [Details]
  • ESANN 1999 - Estimating the intrinsic dimensionality of hyperspectral images [Details]
S. Buchholz
  • ESANN 2000 - Quaternionic spinor MLP [Details]
Lucie Büchi
  • ESANN 2016 - Augmenting a convolutional neural network with local histograms - A case study in crop classification from high-resolution UAV imagery [Details]
Juri Buchmüller
  • ESANN 2018 - G-Rap: interactive text synthesis using recurrent neural network suggestions [Details]
A. Budillon
  • ESANN 1999 - Independent component analysis for mixture densities [Details]
Vicente Buendia-Ramon
  • ESANN 2011 - Analysis of a Reinforcement Learning algorithm using Self-Organizing Maps [Details]
Jean-Luc Buessler
  • ESANN 2013 - Novelty detection in image recognition using IRF Neural Networks properties [Details]
A. Buhot
  • ESANN 1997 - Numerical simulations of an optimal algorithm for supervised learning [Details]
  • ESANN 1999 - Detection of two Gaussian clusters [Details]
  • ESANN 1999 - Statistical mechanics of support vector machine [Details]
David Buldain
  • ESANN 2004 - Visual person tracking with a Supervised Conditioning-SOM [Details]
  • ESANN 2011 - SO-VAT: Self-Organizing Visual Assessment of cluster Tendency for large data sets [Details]
  • ESANN 2012 - magnitude sensitive competitive learning [Details]