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
收藏
页码：1065 / +
页数：2
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