Skip to Main Content

Advertisement

Umbrella Alt Text
Search
Advanced Search
Register
Toggle MenuMenu
Skip Nav Destination

Divergence-Based Vector Quantization

Neural Computation (2011) 23 (5): 1343–1392.
This article has been cited by the following articles in journals that are participating in Crossref Cited-by Linking.
The Resolved Mutual Information Function as a Structural Fingerprint of Biomolecular Sequences for Interpretable Machine Learning Classifiers
  • Katrin Sophie Bohnsack
  • Marika Kaden
  • Julia Abel
  • Sascha Saralajew
  • Thomas Villmann
Entropy (2021) 23 (10): 1357.
Extending the Beta divergence to complex values
  • Colin Vaz
  • Shrikanth Narayanan
Pattern Recognition Letters (2021) 144: 105.
Learning vector quantization as an interpretable classifier for the detection of SARS-CoV-2 types based on their RNA sequences
  • Marika Kaden
  • Katrin Sophie Bohnsack
  • Mirko Weber
  • Mateusz Kudła
  • Kaja Gutowska
  • Jacek Blazewicz
  • Thomas Villmann
Neural Computing and Applications (2021)
Quantum-inspired learning vector quantizers for prototype-based classification
  • Thomas Villmann
  • Alexander Engelsberger
  • Jensun Ravichandran
  • Andrea Villmann
  • Marika Kaden
Neural Computing and Applications (2020)
Convergence of Stochastic Vector Quantization and Learning Vector Quantization with Bregman Divergences
  • Christos N. Mavridis
  • John S. Baras
IFAC-PapersOnLine (2020) 53 (2): 2214.
Density-Dependent Quantized Least Squares Support Vector Machine for Large Data Sets
  • Shengyu Nan
  • Lei Sun
  • Badong Chen
  • Zhiping Lin
  • Kar-Ann Toh
IEEE Transactions on Neural Networks and Learning Systems (2017) 28 (1): 94.
Types of (dis-)similarities and adaptive mixtures thereof for improved classification learning
  • D. Nebel
  • M. Kaden
  • A. Villmann
  • T. Villmann
Neurocomputing (2017) 268: 42.
Can Learning Vector Quantization be an Alternative to SVM and Deep Learning? - Recent Trends and Advanced Variants of Learning Vector Quantization for Classification Learning
  • Thomas Villmann
  • Andrea Bohnsack
  • Marika Kaden
Journal of Artificial Intelligence and Soft Computing Research (2017) 7 (1): 65.
On Hölder Projective Divergences
  • Frank Nielsen
  • Ke Sun
  • Stéphane Marchand-Maillet
Entropy (2017) 19 (3): 122.
Learning matrix quantization and relevance learning based on Schatten-p-norms
  • A. Bohnsack
  • K. Domaschke
  • M. Kaden
  • M. Lange
  • T. Villmann
Neurocomputing (2016) 192: 104.
Fusion trees for fast and accurate classification of hyperspectral data with ensembles of $$\gamma$$ γ -divergence-based RBF networks
  • Uwe Knauer
  • Andreas Backhaus
  • Udo Seiffert
Neural Computing and Applications (2015) 26 (2): 253.
Building the library of RNA 3D nucleotide conformations using the clustering approach
  • Tomasz Zok
  • Maciej Antczak
  • Martin Riedel
  • David Nebel
  • Thomas Villmann
  • Piotr Lukasiak
  • Jacek Blazewicz
  • Marta Szachniuk
International Journal of Applied Mathematics and Computer Science (2015) 25 (3): 689.
Border-sensitive learning in generalized learning vector quantization: an alternative to support vector machines
  • Marika Kaden
  • Martin Riedel
  • Wieland Hermann
  • Thomas Villmann
Soft Computing (2015) 19 (9): 2423.
Non-Euclidean principal component analysis by Hebbian learning
  • Mandy Lange
  • Michael Biehl
  • Thomas Villmann
Neurocomputing (2015) 147: 107.
Robust classification with reject option using the self-organizing map
  • Ricardo Gamelas Sousa
  • Ajalmar R. Rocha Neto
  • Jaime S. Cardoso
  • Guilherme A. Barreto
Neural Computing and Applications (2015) 26 (7): 1603.
Kernelized vector quantization in gradient-descent learning
  • Thomas Villmann
  • Sven Haase
  • Marika Kaden
Neurocomputing (2015) 147: 83.
Median variants of learning vector quantization for learning of dissimilarity data
  • David Nebel
  • Barbara Hammer
  • Kathleen Frohberg
  • Thomas Villmann
Neurocomputing (2015) 169: 295.
Lateral enhancement in adaptive metric learning for functional data
  • T. Villmann
  • M. Kaden
  • D. Nebel
  • M. Riedel
Neurocomputing (2014) 131: 23.
A review of learning vector quantization classifiers
  • David Nova
  • Pablo A. Estévez
Neural Computing and Applications (2014) 25 (3-4): 511.
BREGMAN DIVERGENCES FOR GROWING HIERARCHICAL SELF-ORGANIZING NETWORKS
  • EZEQUIEL LÓPEZ-RUBIO
  • ESTEBAN JOSÉ PALOMO
  • ENRIQUE DOMÍNGUEZ
International Journal of Neural Systems (2014) 24 (04): 1450016.
Normalized estimating equation for robust parameter estimation
  • Hironori Fujisawa
Electronic Journal of Statistics (2013) 7 (none)
Clustering by Fuzzy Neural Gas and Evaluation of Fuzzy Clusters
  • Tina Geweniger
  • Lydia Fischer
  • Marika Kaden
  • Mandy Lange
  • Thomas Villmann
Computational Intelligence and Neuroscience (2013) 2013: 1.
Stochastic neighbor embedding (SNE) for dimension reduction and visualization using arbitrary divergences
  • Kerstin Bunte
  • Sven Haase
  • Michael Biehl
  • Thomas Villmann
Neurocomputing (2012) 90: 23.
Approximation techniques for clustering dissimilarity data
  • Xibin Zhu
  • Andrej Gisbrecht
  • Frank-Michael Schleif
  • Barbara Hammer
Neurocomputing (2012) 90: 72.
Functional relevance learning in generalized learning vector quantization
  • Marika Kästner
  • Barbara Hammer
  • Michael Biehl
  • Thomas Villmann
Neurocomputing (2012) 90: 85.
Generalized Alpha-Beta Divergences and Their Application to Robust Nonnegative Matrix Factorization
  • Andrzej Cichocki
  • Sergio Cruces
  • Shun-ichi Amari
Entropy (2011) 13 (1): 134.

Advertisement

  • Online ISSN 1530-888X
  • Print ISSN 0899-7667

Books

Journals

US

  • One Broadway, 12th Floor
  • Cambridge, MA 02142

Customer Support

Connect

© 2021 The MIT Press
Close Modal
This Feature Is Available To Subscribers Only

or Create an Account

Close Modal
Close Modal
This site uses cookies. By continuing to use our website, you are agreeing to our privacy policy.
Accept