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The regularized LVQ1 algorithm
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DOI：10.1016/j.neucom.2005.12.123.png)
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Abstract
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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.
Keywords:
LVQ1 algorithm
nearest neighbour classifiers
online gradient descent
newton optimization
pattern recognition
Journal
IF:
6.5
Papers:
2.5W
Citations:
7.4W
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NEUROCOMPUTING
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