Finding prototypes for nearest neighbour classification by means of gradient descent and deterministic annealing

被引：28

作者：

Decaestecker, C

机构：

[1] Inst. Interdisc. R. D. Artif. I., Univ. Libre de Bruxelles-CP 194/6, Brussels 1050

来源：

PATTERN RECOGNITION | 1997年 / 30卷 / 02期

关键词：

nearest neighbour; classifier; supervised learning; prototypical reference vectors; optimisation; gradient; annealing;

D O I：

10.1016/S0031-3203(96)00072-6

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A new method is presented to find prototypes for a nearest neighbour classifier. The prototype locations are optimised through a gradient descent and a deterministic annealing process. The proposed algorithm also includes an initialisation strategy which aims to provide the maximum classification rate on the training set with the minimum number of prototypes. Experiments show the efficiency of this algorithm on both real and artificial data. Copyright (C) 1997 Pattern Recognition Society.

引用

页码：281 / 288

页数：8