A batch learning vector quantization algorithm for nearest neighbour classification

被引：11

作者：

Bermejo, S ^{[1
]}

Cabestany, J ^{[1
]}

机构：

[1] Univ Politecn Catalunya, Dept Elect Engn, ES-08034 Barcelona, Spain

来源：

NEURAL PROCESSING LETTERS | 2000年 / 11卷 / 03期

关键词：

Learning Vector Quantization; Newton's optimization; nearest neighbour classification; batch learning algorithms;

D O I：

10.1023/A:1009634824627

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We introduce a batch learning algorithm to design the set of prototypes of 1 nearest-neighbour classifiers. Like Kohonen's LVQ algorithms, this procedure tends to perform vector quantization over a probability density function that has zero points at Bayes borders. Although it differs significantly from their online counterparts since: (1) its statistical goal is clearer and better defined; and (2) it converges superlinearly due to its use of the very fast Newton's optimization method. Experiments results using artificial data confirm faster training time and better classification performance than Kohonen's LVQ algorithms.

引用

页码：173 / 184

页数：12