Self-organizing maps
The handbook of brain theory and neural networks
On-line learning in neural networks
On-line learning in neural networks
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)
Supervised Neural Gas with General Similarity Measure
Neural Processing Letters
Neural maps in remote sensing image analysis
Neural Networks - 2003 Special issue: Neural network analysis of complex scientific data: Astronomy and geosciences
On the Generalization Ability of GRLVQ Networks
Neural Processing Letters
Local metric adaptation for soft nearest prototype classification to classify proteomic data
WILF'05 Proceedings of the 6th international conference on Fuzzy Logic and Applications
Soft nearest prototype classification
IEEE Transactions on Neural Networks
Phase transitions in vector quantization and neural gas
Neurocomputing
Statistical Mechanics of On-line Learning
Similarity-Based Clustering
Distance learning in discriminative vector quantization
Neural Computation
Constrained Learning Vector Quantization or Relaxed k-Separability
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Learning vector quantization with adaptive prototype addition and removal
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Adaptive relevance matrices in learning vector quantization
Neural Computation
Window-based example selection in learning vector quantization
Neural Computation
Improving accuracy of LVQ algorithm by instance weighting
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
White box classification of dissimilarity data
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
Kernel robust soft learning vector quantization
ANNPR'12 Proceedings of the 5th INNS IAPR TC 3 GIRPR conference on Artificial Neural Networks in Pattern Recognition
Learning vector quantization for (dis-)similarities
Neurocomputing
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Learning vector quantization (LVQ) schemes constitute intuitive, powerful classification heuristics with numerous successful applications but, so far, limited theoretical background. We study LVQ rigorously within a simplifying model situation: two competing prototypes are trained from a sequence of examples drawn from a mixture of Gaussians. Concepts from statistical physics and the theory of on-line learning allow for an exact description of the training dynamics in high-dimensional feature space. The analysis yields typical learning curves, convergence properties, and achievable generalization abilities. This is also possible for heuristic training schemes which do not relate to a cost function. We compare the performance of several algorithms, including Kohonen's LVQ1 and LVQ+/-, a limiting case of LVQ2.1. The former shows close to optimal performance, while LVQ+/- displays divergent behavior. We investigate how early stopping can overcome this difficulty. Furthermore, we study a crisp version of robust soft LVQ, which was recently derived from a statistical formulation. Surprisingly, it exhibits relatively poor generalization. Performance improves if a window for the selection of data is introduced; the resulting algorithm corresponds to cost function based LVQ2. The dependence of these results on the model parameters, for example, prior class probabilities, is investigated systematically, simulations confirm our analytical findings.