ON SIMPLE ONE-CLASS CLASSIFICATION METHODS

被引：0

作者：

Noumir, Zineb ^{[1
]}

Honeine, Paul ^{[1
]}

Richard, Cedric ^{[2
]}

机构：

[1] Univ Technol Troyes, Inst Charles Delaunay, F-10010 Troyes, France

[2] Univ Nice Sophia Antipolis, Lab H Fizeau, F-06108 Nice, France

来源：

2012 IEEE INTERNATIONAL SYMPOSIUM ON INFORMATION THEORY PROCEEDINGS (ISIT) | 2012年

关键词：

SUPPORT VECTOR MACHINE;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

The one-class classification has been successfully applied in many communication, signal processing, and machine learning tasks. This problem, as defined by the one-class SVM approach, consists in identifying a sphere enclosing all (or the most) of the data. The classical strategy to solve the problem considers a simultaneous estimation of both the center and the radius of the sphere. In this paper, we study the impact of separating the estimation problem. It turns out that simple one-class classification methods can be easily derived, by considering a least-squares formulation. The proposed framework allows us to derive some theoretical results, such as an upper bound on the probability of false detection. The relevance of this work is illustrated on well-known datasets.

引用

页数：5

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