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
M. Bogdan
  • ESANN 2001 - Detection of cluster in Self-Organizing Maps for controlling a prostheses using nerve signals [Details]
  • ESANN 2003 - Towards the restoration of hand grasp function of quadriplegic patients based on an artificial neural net controller using peripheral nerve stimulation - an approach [Details]
Martin Bogdan
  • ESANN 2023 - Is Boredom an Indicator on the way to Singularity of Artificial Intelligence? Hypotheses as Thought-Provoking Impulse [Details]
  • ESANN 2023 - Sleep analysis in a CLIS patient using soft-clustering: a case study [Details]
  • ESANN 2025 - A Pipeline based on Differential Evolution for Tuning Parameters of Synaptic Dynamics Models [Details]
  • ESANN 2025 - The Regulatory Character of Boredom in AI - Towards a Self-Regulating System based on Spiking Neural Networks [Details]
  • ESANN 2025 - The Reinforced Liquid State Machine: A New Training Architecture for Spiking Neural Networks [Details]
  • ESANN 2026 - Exploring the Relationship Between Synaptic Dynamics Properties: Gain-Control and Temporal Filtering [Details]
Martin Bogdan
  • ESANN 2005 - Feature selection for high-dimensional industrial data [Details]
  • ESANN 2006 - Artificial neural networks and machine learning for man-machine-interfaces - processing of nervous signals [Details]
  • ESANN 2008 - Direct and inverse solution for a stimulus adaptation problem using SVR [Details]
  • ESANN 2010 - Predicting spike-timing of a thalamic neuron using a stochastic synaptic model [Details]
  • ESANN 2011 - Classifying mental states with machine learning algorithms using alpha activity decline [Details]
  • ESANN 2012 - One Class SVM and Canonical Correlation Analysis increase performance in a c-VEP based Brain-Computer Interface (BCI) [Details]
  • ESANN 2013 - Decoding stimulation intensity from evoked ECoG activity using support vector regression [Details]
  • ESANN 2013 - Efficient prediction of x-axis intercepts of discrete impedance spectra [Details]
  • ESANN 2017 - Collaborative filtering with neural networks [Details]
  • ESANN 2018 - Neural Networks for Implicit Feedback Datasets [Details]
Martin Böhm
  • ESANN 2021 - An Algorithmic Approach to Establish a Lower Bound for the Size of Semiring Neural Networks [Details]
Katrin Sophie Bohnsack
  • ESANN 2022 - Efficient classification learning of biochemical structured data by means of relevance weighting for sensoric response features [Details]
  • ESANN 2023 - Sparse Nyström Approximation for Non-Vectorial Data Using Class-informed Landmark Selection [Details]
Katrin Sophie Bohnsack
  • No papers found
S. M. Bohte
  • ESANN 2000 - SpikeProp: backpropagation for networks of spiking neurons [Details]
S.M. Bohte
  • ESANN 2002 - Modeling efficient conjunction detection with spiking neural networks [Details]
Sander Bohte
  • ESANN 2014 - Learning resets of neural working memory [Details]
  • ESANN 2014 - Spiking AGREL [Details]
  • ESANN 2014 - Spiking Neural Networks: Principles and Challenges [Details]
Luis Eduardo Boiko
  • ESANN 2018 - Adaptive random forests for data stream regression [Details]
Hugo Boisaubert
  • ESANN 2025 - Investigating four deep learning approaches as candidates for unified models in time series forecasting and event prediction: application in anesthesia training [Details]
J.-M. Boite
  • ESANN 1999 - Development of a French speech recognizer using a hybrid HMM/MLP system [Details]
T. Bojer
  • ESANN 2001 - Relevance determination in Learning Vector Quantization [Details]
  • ESANN 2003 - Monitoring technical systems with prototype based clustering [Details]
Gianluca Boleto
  • ESANN 2021 - In-Station Train Movements Prediction: from Shallow to Deep Multi Scale Models [Details]
D. Bollé
  • ESANN 1993 - Mixture states in Potts neural networks [Details]
  • ESANN 1993 - Paralell dynamics of extremely diluted neural networks [Details]
Felix Bollenbeck
  • ESANN 2006 - Fuzzy image segmentation with Fuzzy Labelled Neural Gas [Details]
D. Bollini
  • ESANN 2000 - Automatic detection of clustered microcalcifications in digital mammograms using an SVM classifier [Details]
Verónica Bolón-Canedo
  • No papers found
Veronica Bolon-Canedo
  • ESANN 2011 - Statistical dependence measure for feature selection in microarray datasets [Details]
  • ESANN 2013 - A distributed wrapper approach for feature selection [Details]
  • ESANN 2014 - Toward parallel feature selection from vertically partitioned data [Details]
  • ESANN 2015 - Feature and kernel learning [Details]
  • ESANN 2015 - Learning features on tear film lipid layer classification [Details]
  • ESANN 2015 - On the use of machine learning techniques for the analysis of spontaneous reactions in automated hearing assessment [Details]
  • ESANN 2016 - Data complexity measures for analyzing the effect of SMOTE over microarrays [Details]
  • ESANN 2016 - Machine learning for medical applications [Details]
  • ESANN 2016 - Using a feature selection ensemble on DNA microarray datasets [Details]
  • ESANN 2017 - A distributed approach for classification using distance metrics [Details]
  • ESANN 2017 - Algorithmic challenges in big data analytics [Details]
  • ESANN 2018 - Analysis of imputation bias for feature selection with missing data [Details]
Verónica Bolón-Canedo
  • ESANN 2022 - Feature selection for transfer learning using particle swarm optimization and complexity measures [Details]
  • ESANN 2022 - The role of feature selection in personalized recommender systems [Details]
  • ESANN 2023 - Automated green machine learning for condition-based maintenance [Details]
  • ESANN 2023 - Efficient feature selection for domain adaptation using Mutual Information Maximization [Details]
  • ESANN 2023 - Green Machine Learning [Details]
  • ESANN 2023 - Logarithmic division for green feature selection: an information-theoretic approach [Details]
  • ESANN 2026 - Information-Theoretic Unsupervised Feature Selection for High-Dimensional Spatial Data [Details]
  • ESANN 2026 - SMOTE k-out: Enhancing Class Separability through Outer Synthetic Sampling [Details]
Verónica Bolón-Canedo
  • ESANN 2020 - Do we need hundreds of classifiers or a good feature selection? [Details]
  • ESANN 2025 - Do not get lost in projection: finding the right distance for meaningful UMAP embeddings [Details]
L. Bolshov
  • ESANN 2000 - Application of MLP and stochastic simulations for electricity load forecasting in Russia [Details]
Michael Bon
  • ESANN 2020 - Estimating Individual Treatment Effects through Causal Populations Identification [Details]
R. Bone
  • ESANN 1998 - NAR time-series prediction: a Bayesian framework and an experiment [Details]
  • ESANN 2000 - An algorithm for the addition of time-delayed connections to recurrent neural networks [Details]
Simone Bonechi
  • ESANN 2020 - Graph Neural Networks for the Prediction of Protein-Protein Interfaces [Details]
  • ESANN 2022 - A weakly supervised approach to skin lesion segmentation [Details]
  • ESANN 2022 - Deep Learning Approaches for mice glomeruli segmentation [Details]
  • ESANN 2025 - Leveraging Segmentation Maps to improve Skin Lesion Classification [Details]
José Bonelo
  • ESANN 2008 - Multilayer perceptron to model the decarburization process in stainless steel production [Details]
Isis Bonet
  • No papers found
Giovanni Bonetta
  • ESANN 2024 - Vision Language Models as Policy Learners in Reinforcement Learning Environments [Details]
  • ESANN 2025 - A feedback-loop approach for galaxy physical properties estimation [Details]
Giovanni Bonetta
  • No papers found
Giovanni Bonetta
  • ESANN 2022 - Lightening CNN architectures by regularization driven weights' pruning [Details]