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摘要
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This paper introduces a straightforward generalization of the well-known LVQ1 algorithm for nearest neighbour classifiers that includes the standard LVQ1 and tile k-means algorithms as special cases. It is based on a regularizing parameter that monotonically decreases the upper bound of the training classification error towards a minimum. Experiments using 10 real data sets show the utility of this simple extension of LVQ1. (c) 2006 Elsevier B.V. All rights reserved.
Keyword:
LVQ1 algorithm
nearest neighbour classifiers
online gradient descent
newton optimization
pattern recognition
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期刊
IF:
6.5
论文数:
2.5W
被引数:
7.3W
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