K-NN algorithm based on neural similarity

被引：0

作者：

Lazzerini, B ^{[1
]}

Marcelloni, F ^{[1
]}

机构：

[1] Univ Pisa, Dipartimento Ingn Informaz, I-56122 Pisa, Italy

来源：

2002 IEEE INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE SYSTEMS, PROCEEDINGS | 2002年

关键词：

D O I：

10.1109/ICAIS.2002.1048054

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The aim of this paper is to present a k-nearest neighbour (k-NN) classifier based on a neural model of the similarity measure between data. After a preliminary phase of supervised learning for similarity determination, we use the neural similarity measure to guide the k-NN rule. Experiments on both synthetic and real-world data show that the similarity-based k-NN rule outperforms the Euclidean distance-based k-NN rule.

引用

页码：67 / 70

页数：4

共 9 条

[1]

AHA D, 1998, FEATURE EXTRACTION C

[2] NEAREST NEIGHBOR PATTERN CLASSIFICATION [J].