A support vector clustering method

被引：0

作者：

Ben-Hur, A ^{[1
]}

Horn, D ^{[1
]}

Siegelmann, HT ^{[1
]}

Vapnik, V ^{[1
]}

机构：

[1] Technion Israel Inst Technol, Fac Ind Engn & Management, IL-32000 Haifa, Israel

来源：

15TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 2, PROCEEDINGS: PATTERN RECOGNITION AND NEURAL NETWORKS | 2000年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present a novel kernel method for data clustering using a description of the data by support vectors. The kernel reflects a projection of the data points from data space to a high dimensional feature space. Cluster boundaries are defined as spheres in feature space, which represent complex geometric shapes in data space. We utilize this geometric representation of the data to construct a simple clustering algorithm.

引用

页码：724 / 727

页数：2

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