Kernel nearest-neighbor algorithm

被引：104

作者：

Yu, K ^{[1
]}

Ji, L ^{[1
]}

Zhang, XG ^{[1
]}

机构：

[1] Tsing Hua Univ, Dept Automat, Inst Informat Proc, State Key Lab Intelligent Technol & Syst, Beijing 100084, Peoples R China

来源：

NEURAL PROCESSING LETTERS | 2002年 / 15卷 / 02期

基金：

中国国家自然科学基金;

关键词：

kernel; nearest-neighbor; nonlinear classification;

D O I：

10.1023/A:1015244902967

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The 'kernel approach' has attracted great attention with the development of support vector machine (SVM) and has been studied in a general way. It offers an alternative solution to increase the computational power of linear learning machines by mapping data into a high dimensional feature space. This 'approach' is extended to the well-known nearest-neighbor algorithm in this paper. It can be realized by substitution of a kernel distance metric for the original one in Hilbert space, and the corresponding algorithm is called kernel nearest-neighbor algorithm. Three data sets, an artificial data set, BUPA liver disorders database and USPS database, were used for testing. Kernel nearest-neighbor algorithm was compared with conventional nearest-neighbor algorithm and SVM Experiments show that kernel nearest-neighbor algorithm is more powerful than conventional nearest-neighbor algorithm, and it can compete with SVM.

引用

页码：147 / 156

页数：10