期刊
NEURAL COMPUTING & APPLICATIONS
卷 25, 期 3-4, 页码 511-524出版社
SPRINGER LONDON LTD
DOI: 10.1007/s00521-013-1535-3
关键词
Learning vector quantization; Supervised learning; Neural networks; Margin maximization; Likelihood ratio maximization
资金
- CONICYT-CHILE [FONDECYT 1110701]
In this work, we present a review of the state of the art of learning vector quantization (LVQ) classifiers. A taxonomy is proposed which integrates the most relevant LVQ approaches to date. The main concepts associated with modern LVQ approaches are defined. A comparison is made among eleven LVQ classifiers using one real-world and two artificial datasets.
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