Self-organizing maps
Self-Organizing Maps and Learning Vector Quantization forFeature Sequences
Neural Processing Letters
Statistical Mechanics of Learning
Statistical Mechanics of Learning
Generalized relevance learning vector quantization
Neural Networks - New developments in self-organizing maps
Soft learning vector quantization
Neural Computation
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
On the Generalization Ability of GRLVQ Networks
Neural Processing Letters
On-Line Learning in Neural Networks
On-Line Learning in Neural Networks
Soft nearest prototype classification
IEEE Transactions on Neural Networks
Performance analysis of LVQ algorithms: a statistical physics approach
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Margin-based active learning for LVQ networks
Neurocomputing
Statistical Mechanics of On-line Learning
Similarity-Based Clustering
Optimized Learning Vector Quantization Classifier with an Adaptive Euclidean Distance
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Adaptive relevance matrices in learning vector quantization
Neural Computation
Harnessing ANN for a secure environment
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
Energy-based function to evaluate data stream clustering
Advances in Data Analysis and Classification
Data stream dynamic clustering supported by Markov chain isomorphisms
Intelligent Data Analysis
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Winner-Takes-All (WTA) prescriptions for learning vector quantization (LVQ) are studied in the framework of a model situation: two competing prototype vectors are updated according to a sequence of example data drawn from a mixture of Gaussians. The theory of on-line learning allows for an exact mathematical description of the training dynamics, even if an underlying cost function cannot be identified. We compare the typical behavior of several WTA schemes including basic LVQ and unsupervised vector quantization. The focus is on the learning curves, i.e. the achievable generalization ability as a function of the number of training examples.