Deterministic Annealing Multi-Sphere Support Vector Data Description

被引：0
|
作者：
Trung Le [1 ]
Dat Tran [1 ]
Ma, Wanli [1 ]
Sharma, Dharmendra [1 ]
机构：
[1] Univ Canberra, Canberra, ACT 2601, Australia
来源：
NEURAL INFORMATION PROCESSING, ICONIP 2012, PT III | 2012年 / 7665卷
关键词：
Kernel Methods; Deterministic Annealing; Support Vector Data Description; Multi-Sphere Support Vector Data Description;
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：
Current well-known data description method such as Support Vector Data Description is conducted with assumption that data samples of a class in feature space are drawn from a single distribution. Based on this assumption, a single hypersphere is constructed to provide a good data description for the data. However, real-world data samples may be drawn from some distinctive distributions and hence it does not guarantee that a single hypersphere can offer the best data description. In this paper, we introduce a Deterministic Annealing Multi-sphere Support Vector Data Description (DAMS-SVDD) approach to address this issue. We propose to use a set of hyperspheres to provide a better data description for a given data set. Calculations for determining optimal hyperspheres and experimental results for applying this proposed approach to classification problems are presented.
引用
收藏
页码：183 / 190
页数：8
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