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
Sven Haase
  • ESANN 2010 - Divergence based Learning Vector Quantization [Details]
  • ESANN 2010 - Learning vector quantization for heterogeneous structured data [Details]
  • ESANN 2011 - Mathematical Foundations of the Self Organized Neighbor Embedding (SONE) for Dimension Reduction and Visualization [Details]
Zineb Habbas
  • No papers found
Ben Hack
  • ESANN 2021 - SmoothLRP: Smoothing LRP by Averaging over Stochastic Input Variations [Details]
Christodoulos Hadjichristodoulou
  • ESANN 2024 - Interpreting Hybrid AI through Autodecoded Latent Space Entities [Details]
Muhammad Burhan Hafez
  • ESANN 2018 - Slowness-based neural visuomotor control with an Intrinsically motivated Continuous Actor-Critic [Details]
P. Häfliger
  • ESANN 1999 - Learning a temporal code [Details]
Aya HAGE CHEHADE
  • ESANN 2025 - O-Net: a Brain Tumor segmentation architecture based on U-Net using alternated Pooling [Details]
M. Hagenbuchner
  • ESANN 1999 - A benchmark for testing adaptive systems on structured data [Details]
Markus Hagenbuchner
  • ESANN 2005 - Contextual Processing of Graphs using Self-Organizing Maps [Details]
  • ESANN 2007 - "Kernelized" Self-Organizing Maps for Structured Data [Details]
  • ESANN 2008 - Self-Organizing Maps for cyclic and unbounded graphs [Details]
  • ESANN 2011 - Sparsity Issues in Self-Organizing-Maps for Structures [Details]
  • ESANN 2013 - Cost-sensitive cascade graph neural networks [Details]
Markus Hagenbuchner
  • ESANN 2020 - Embedding of FRPN in CNN architecture [Details]
Lukas Hahn
  • ESANN 2019 - Fast and reliable architecture selection for convolutional neural networks [Details]
K. Hahn
  • ESANN 2000 - A neural network approach to adaptive pattern analysis - the deformable feature map [Details]
  • ESANN 2001 - Analysis of dynamic perfusion MRI data by neural networks [Details]
Janne Hahne
  • ESANN 2017 - An EM transfer learning algorithm with applications in bionic hand prostheses [Details]
Carolin Hainke
  • ESANN 2019 - Dynamic fairness - Breaking vicious cycles in automatic decision making [Details]
Hannu Häkkinen
  • ESANN 2021 - Orientation Adaptive Minimal Learning Machine for Directions of Atomic Forces [Details]
E. Halasz
  • ESANN 2000 - An optimization neural network model with time-dependent and lossy dynamics [Details]
T. Halawani
  • ESANN 2002 - Use of artificial neural networks process analyzers: a case study [Details]
John Hallam
  • ESANN 2006 - Evolving multi-segment 'super-lamprey' CPG's for increased swimming control [Details]
Rasmus Halland
  • ESANN 2015 - High-School Dropout Prediction Using Machine Learning: A Danish Large-scale Study [Details]
Sandra Halscheidt
  • ESANN 2023 - Segmentation and Analysis of Lumbar Spine MRI Scans for Vertebral Body Measurements [Details]
Valentin Hamaide
  • ESANN 2021 - Transfer learning in Bayesian optimization for the calibration of a beam line in proton therapy [Details]
Joonas Hämäläinen
  • ESANN 2020 - Problem Transformation Methods with Distance-Based Learning for Multi-Target Regression [Details]
  • ESANN 2021 - Instance-Based Multi-Label Classification via Multi-Target Distance Regression [Details]
  • ESANN 2021 - Orientation Adaptive Minimal Learning Machine for Directions of Atomic Forces [Details]
  • ESANN 2023 - Feature Selection for Multi-label Classification with Minimal Learning Machine [Details]
Joonas Hämäläinen
  • ESANN 2016 - Initialization of big data clustering using distributionally balanced folding [Details]
  • ESANN 2018 - Scalable robust clustering method for large and sparse data [Details]
Leila Hamdad
  • No papers found
Rawaa Hamdi
  • ESANN 2021 - CAS-Net: A Novel Coronary Artery Segmentation Neural Network [Details]
Fatma Hamdi
  • ESANN 2010 - Consensus clustering by graph based approach [Details]
Freddie C. Hamdy
  • ESANN 2005 - Artificial intelligence techniques for the prediction of bladder cancer progression [Details]
Victor Hamer
  • ESANN 2020 - Joint optimization of predictive performance and selection stability [Details]
Yasir Hamid
  • ESANN 2017 - Large-scale nonlinear dimensionality reduction for network intrusion detection [Details]
Barbara Hammer
  • ESANN 2004 - self-organizing context learning [Details]
  • ESANN 2005 - Classification using non-standard metrics [Details]
  • ESANN 2005 - Relevance determination in reinforcement learning [Details]
  • ESANN 2005 - Relevance learning for mental disease classification [Details]
  • ESANN 2005 - The dynamics of Learning Vector Quantization [Details]
  • ESANN 2006 - Magnification control for batch neural gas [Details]
  • ESANN 2006 - Margin based Active Learning for LVQ Networks [Details]
  • ESANN 2006 - Neural networks and machine learning in bioinformatics - theory and applications [Details]
  • ESANN 2007 - How to process uncertainty in machine learning? [Details]
  • ESANN 2007 - On the dynamics of Vector Quantization and Neural Gas [Details]
  • ESANN 2007 - Relevance matrices in LVQ [Details]
  • ESANN 2008 - Magnification Control in Relational Neural Gas [Details]
  • ESANN 2008 - Parallelizing single patch pass clustering [Details]
  • ESANN 2009 - Equilibrium properties of off-line LVQ [Details]
  • ESANN 2009 - Hyperparameter Learning in Robust Soft LVQ [Details]
  • ESANN 2009 - Median Variant of Fuzzy c-Means [Details]
  • ESANN 2009 - Nonlinear Discriminative Data Visualization [Details]
  • ESANN 2009 - Recent advances in efficient learning of recurrent networks [Details]
  • ESANN 2010 - Exploratory Observation Machine (XOM) with Kullback-Leibler Divergence for Dimensionality Reduction and Visualization [Details]
  • ESANN 2010 - Relational Generative Topographic Map [Details]
  • ESANN 2010 - Relevance learning in generative topographic maps [Details]
  • ESANN 2010 - Sparse representation of data [Details]
  • ESANN 2011 - Generalized functional relevance learning vector quantization [Details]
  • ESANN 2011 - Patch Affinity Propagation [Details]
  • ESANN 2011 - Supervised dimension reduction mappings [Details]
  • ESANN 2012 - Out-of-sample kernel extensions for nonparametric dimensionality reduction [Details]
  • ESANN 2012 - Recent developments in clustering algorithms [Details]
  • ESANN 2012 - Relevance learning for time series inspection [Details]
  • ESANN 2012 - Visualizing the quality of dimensionality reduction [Details]
  • ESANN 2013 - Semi-Supervised Vector Quantization for proximity data [Details]
  • ESANN 2013 - Sparse approximations for kernel learning vector quantization [Details]
  • ESANN 2013 - Visualizing dependencies of spectral features using mutual information [Details]
  • ESANN 2014 - Adaptive distance measures for sequential data [Details]
  • ESANN 2014 - Learning and modeling big data [Details]
  • ESANN 2014 - Rejection strategies for learning vector quantization [Details]
  • ESANN 2014 - Relevance Learning for Dimensionality Reduction [Details]
  • ESANN 2014 - Supervised Generative Models for Learning Dissimilarity Data [Details]
  • ESANN 2015 - Adaptive structure metrics for automated feedback provision in Java programming [Details]
  • ESANN 2015 - Certainty-based prototype insertion/deletion for classification with metric adaptation [Details]
  • ESANN 2015 - Unsupervised Dimensionality Reduction for Transfer Learning [Details]
  • ESANN 2016 - Choosing the best algorithm for an incremental on-line learning task [Details]
  • ESANN 2016 - Discriminative dimensionality reduction in kernel space [Details]
  • ESANN 2016 - Gaussian process prediction for time series of structured data [Details]
  • ESANN 2016 - Incremental learning algorithms and applications [Details]
  • ESANN 2017 - An EM transfer learning algorithm with applications in bionic hand prostheses [Details]
  • ESANN 2017 - Feature Relevance Bounds for Linear Classification [Details]
  • ESANN 2018 - Differential private relevance learning [Details]
  • ESANN 2018 - Feasibility based Large Margin Nearest Neighbor metric learning [Details]
  • ESANN 2019 - Feature relevance bounds for ordinal regression [Details]
  • ESANN 2019 - Multiple-Kernel dictionary learning for reconstruction and clustering of unseen multivariate time-series [Details]
  • ESANN 2019 - Recent trends in streaming data analysis, concept drift and analysis of dynamic data sets [Details]