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

来源：

PATTERN RECOGNITION LETTERS | 2005年 / 26卷 / 08期

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

页码：1029 / 1038

页数：10