Optimal feature representation for kernel machines using kernel-target alignment criterion

被引：0

作者：

Pothin, Jean-Baptiste ^{[1
]}

Richard, Cedric ^{[1
]}

机构：

[1] Univ Technol Troyes, Inst Charles Delaunay, ICD M2S FRE 2848, CNRS, 12 Rue Marie Curie,BP 2060, F-10010 Troyes, France

来源：

2007 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOL III, PTS 1-3, PROCEEDINGS | 2007年

关键词：

pattern classification; alignment; SVM;

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

Kernel-target alignment is commonly used to predict the behavior of any given reproducing kernel in a classification context, without training any kernel machine. In this paper, we present a gradient ascent algorithm for maximizing the alignment over linear transform of the input space. Our method is compared to the minimization of the radius-margin bound. Experimental results on multi-dimensional benchmarks show the effectiveness of our approach.

引用

页码：1065 / +

页数：2

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