Support vector machine-based image classification for genetic syndrome diagnosis
Support vector machine-based image classification for genetic syndrome diagnosis
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DOI：
10.1016/j.patrec.2004.09.048
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发表时间：
2005-06-01
影响因子：
5.1
通讯作者：
Lerner, B
中科院分区：
文献类型：
--
作者：
David, A;Lerner, B
为了优化支持向量机和基于多类支持向量机的图像分类器的核函数和参数，我们进行了结构风险最小化和交叉验证，从而实现了对遗传异常的诊断。通过将模式与分类分离假设的距离设为阈值，我们拒绝了一定比例的未分类模式，从而降低了预期风险。与其他最先进的分类器相比，支持向量机的准确性能证明了基于支持向量机的遗传综合征诊断的好处。(C)2004爱思唯尔B.V.保留所有权利。
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.