Applied multivariate statistical analysis
Applied multivariate statistical analysis
Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
The nature of statistical learning theory
The nature of statistical learning theory
The handbook of brain theory and neural networks
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Self-Organising Neural Networks: Independent Component Analysis and Blind Source Separation
Self-Organising Neural Networks: Independent Component Analysis and Blind Source Separation
Self-Organizing Maps
On the quest for easy-to-understand splitting rules
Data & Knowledge Engineering
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)
Neural-network feature selector
IEEE Transactions on Neural Networks
Input feature selection for classification problems
IEEE Transactions on Neural Networks
New results on observations selection
SMO'07 Proceedings of the 7th WSEAS International Conference on Simulation, Modelling and Optimization
Improved batch fuzzy learning vector quantization for image compression
Information Sciences: an International Journal
Particle swarm optimization for prototype reduction
Neurocomputing
A novel parametric fuzzy CMAC network and its applications
Applied Soft Computing
An Online Incremental Learning Vector Quantization
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Cluster-based nearest-neighbour classifier and its application on the lightning classification
Journal of Computer Science and Technology
Regularized margin-based conditional log-likelihood loss for prototype learning
Pattern Recognition
A multiobjective simultaneous learning framework for clustering and classification
IEEE Transactions on Neural Networks
A new local-global approach for classification
Neural Networks
Weighted learning vector quantization to cost-sensitive learning
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Influence of class distribution on cost-sensitive learning: A case study of bankruptcy analysis
Intelligent Data Analysis
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In this paper, we propose a method that selects a subset of the training data points to update LVQ prototypes. The main goal is to conduct the prototypes to converge at a more convenient location, diminishing misclassification errors. The method selects an update set composed by a subset of points considered to be at the risk of being captured by another class prototype. We associate the proposed methodology to a weighted norm, instead of the Euclidean, in order to establish different levels of relevance for the input attributes. The technique was implemented on a controlled experiment and on Web available data sets.