Kernel-Based Methods for Hypothesis Testing

被引：42

作者：

Harchaoui, Zaid ^{[1
]}

Bach, Francis ^{[2
]}

Cappe, Olivier ^{[3
,4
]}

Moulines, Eric ^{[5
]}

机构：

[1] INRIA, LEAR Team, Lab Jean Kuntzmann, Grenoble, France

[2] Ecole Normale Super, Sierra Project Team, INRIA, Dept Comp Sci, F-75231 Paris, France

[3] CNRS, Joint Lab, Lab Traitement & Commun Informat, F-75700 Paris, France

[4] Telecom ParisTech, Paris, France

[5] ENST, Paris, France

来源：

IEEE SIGNAL PROCESSING MAGAZINE | 2013年 / 30卷 / 04期

基金：

欧洲研究理事会;

关键词：

AUDIO; CONSISTENCY; RATIO;

D O I：

10.1109/MSP.2013.2253631

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Kernel-based methods provide a rich and elegant framework for developing nonparametric detection procedures for signal processing. Several recently proposed procedures can be simply described using basic concepts of reproducing kernel Hilbert space (RKHS) embeddings of probability distributions, mainly mean elements and covariance operators. We propose a unified view of these tools and draw relationships with information divergences between distributions. © 1991-2012 IEEE.

引用

页码：87 / 97

页数：11

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