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
Subrahmanyam K. V.
  • ESANN 2017 - Invariant representations of images for better learning [Details]
Ata Kaban
  • ESANN 2011 - Multi-class classification in the presence of labelling errors [Details]
  • ESANN 2014 - A comprehensive introduction to label noise [Details]
Markus Kächele
  • ESANN 2015 - SMO Lattices for the Parallel Training of Support Vector Machines [Details]
Rostom Kachouri
  • ESANN 2023 - Retinal blood vessel segmentation from high resolution fundus image using deep learning architecture [Details]
Gregor Kaczor
  • ESANN 2009 - Stimulus processing and unsupervised learning in autonomously active recurrent networks [Details]
Sara Kaczynska
  • ESANN 2020 - Comparison of Cluster Validity Indices and Decision Rules for Different Degrees of Cluster Separation [Details]
Marika Kaden
  • ESANN 2014 - Optimization of General Statistical Accuracy Measures for Classification Based on Learning Vector Quantization [Details]
  • ESANN 2015 - Learning matrix quantization and variants of relevance learning [Details]
  • ESANN 2016 - Adaptive dissimilarity weighting for prototype-based classification optimizing mixtures of dissimilarities [Details]
  • ESANN 2018 - Reliable Patient Classification in Case of Uncertain Class Labels Using a Cross-Entropy Approach [Details]
Marika Kaden
  • ESANN 2020 - Quantum-Inspired Learning Vector Quantization for Classification Learning [Details]
  • ESANN 2021 - RecLVQ: Recurrent Learning Vector Quantization [Details]
  • ESANN 2021 - The LVQ-based Counter Propagation Network -- an Interpretable Information Bottleneck Approach [Details]
  • ESANN 2022 - Efficient classification learning of biochemical structured data by means of relevance weighting for sensoric response features [Details]
  • ESANN 2023 - Variants of Neural Gas for Regression Learning [Details]
  • ESANN 2024 - Domain Knowledge Integration in Machine Learning Systems - An Introduction [Details]
  • ESANN 2025 - Integrating Class Relation Knowledge in Probabilistic Learning Vector Quantization [Details]
  • ESANN 2025 - Mitigating the Bias in Data for Fairness Using an Advanced Generalized Learning Vector Quantization Approach -- FA(IR)$^2$MA-GLVQ [Details]
Christoph Käding
  • ESANN 2016 - Watch, Ask, Learn, and Improve: a lifelong learning cycle for visual recognition [Details]
Rebecca Kador
  • ESANN 2023 - Segmentation and Analysis of Lumbar Spine MRI Scans for Vertebral Body Measurements [Details]
Hachem Kadri
  • ESANN 2015 - Online Learning with Operator-valued Kernels [Details]
Marika Kaestner
  • ESANN 2011 - Generalized functional relevance learning vector quantization [Details]
  • ESANN 2011 - Optimization of Parametrized Divergences in Fuzzy c-Means [Details]
Hella Kaffel Ben Ayed
  • ESANN 2025 - Proactive Privacy Risk Assessment for Android Applications: A Machine Learning Based-Approach [Details]
Markus Kaiser
  • ESANN 2019 - interpretable dynamics models for data-efficient reinforcement learning [Details]
Alexander Kaiser
  • ESANN 2010 - Adaptive learning rate control for "neural gas principal component analysis" [Details]
  • ESANN 2010 - Distance functions for local PCA methods [Details]
Virpi Kalakoski
  • ESANN 2015 - Enhancing learning at work. How to combine theoretical and data-driven approaches, and multiple levels of data? [Details]
Jonas Kalderstam
  • ESANN 2013 - Ensembles of genetically trained artificial neural networks for survival analysis [Details]
R. Kallel
  • ESANN 2000 - Bootstrap for neural model selection [Details]
  • ESANN 2002 - Parametric bootstrap for test of contrast difference in neural networks [Details]
H. Kalte
  • ESANN 2002 - A reconfigurable SOM hardware accelerator [Details]
Robert Kaltenhaeuser
  • ESANN 2013 - Evolutionary computation based system decomposition with neural networks [Details]
K.T. Kalveram
  • ESANN 2001 - Motor control and movement optimization learned by combining auto-imitative and genetic algorithms [Details]
K. Th. Kalveram
  • ESANN 2002 - The problem of adaptive control in a living system or how to acquire an inverse model without external help [Details]
W. Kaminski
  • ESANN 2001 - Neural networks with orthogonalised transfer functions [Details]
David Kämpf
  • ESANN 2004 - knowledge discovery in DNA microarray data of cancer patients with emergent self organizing maps [Details]
Mikhail Kanevski
  • ESANN 2020 - Model Variance for Extreme Learning Machine [Details]
  • ESANN 2020 - On Feature Selection Using Anisotropic General Regression Neural Network [Details]
Mikhaïl Kanevski
  • ESANN 2006 - Pattern analysis in illicit heroin seizures: a novel application of machine learning algorithms [Details]
  • ESANN 2008 - GeoKernels: modeling of spatial data on geomanifolds [Details]
  • ESANN 2010 - Machine learning analysis and modeling of interest rate curves [Details]
  • ESANN 2010 - Time series input selection using multiple kernel learning [Details]
  • ESANN 2013 - Multi-view feature extraction for hyperspectral image classification [Details]
  • ESANN 2014 - Feature selection in environmental data mining combining Simulated Annealing and Extreme Learning Machine [Details]
  • ESANN 2015 - Morisita-based feature selection for regression problems [Details]
  • ESANN 2018 - A novel filter algorithm for unsupervised feature selection based on a space filling measure [Details]
M. Kanevski
  • ESANN 2000 - Application of MLP and stochastic simulations for electricity load forecasting in Russia [Details]
Bo Kang
  • ESANN 2016 - Informative data projections: a framework and two examples [Details]
Juho Kannala
  • No papers found
Juho Kannala
  • ESANN 2022 - Adaptive Behavior Cloning Regularization for Stable Offline-to-Online Reinforcement Learning [Details]