Support vector machine-based image classification for genetic syndrome diagnosis

被引:28
|
作者
David, A [1 ]
Lerner, B [1 ]
机构
[1] Ben Gurion Univ Negev, Pattern Anal & Machine Learning Lab, Dept Elect & Comp Engn, IL-84105 Beer Sheva, Israel
关键词
support vector machine (SVM); multiclass classification by error correcting output code (ECOC); rejection; fluorescence in situ hybridization (FISH); genetics;
D O I
10.1016/j.patrec.2004.09.048
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We implement structural risk minimization and cross-validation in order to optimize kernel and parameters of a support vector machine (SVM) and multiclass SVM-based image classifiers, thereby enabling the diagnosis of genetic abnormalities. By thresholding the distance of patterns from the hypothesis separating the classes we reject a percentage of the miss-classified patterns reducing the expected risk. Accurate performance of the SVM in comparison to other state-of-the-art classifiers demonstrates the benefit of SVM-based genetic syndrome diagnosis. (c) 2004 Elsevier B.V. All rights reserved.
引用
收藏
页码:1029 / 1038
页数:10
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