JSM 2004 - Toronto

Abstract #301037

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Activity Number: 88
Type: Contributed
Date/Time: Monday, August 9, 2004 : 8:30 AM to 10:20 AM
Sponsor: Biopharmaceutical Section
Abstract - #301037
Title: Data-adaptive Tuning of Multicategory Support Vector Machines
Author(s): Eway Y. Zhan*+
Companies: Rosetta Biosoftware
Address: 12220, Suite 210, Kirkland, WA, 98034,
Keywords: gene expression profiles ; microarray measurements ; kernel machine ; supervised learning ; cancer classification ; diagnostic prediction of cancer
Abstract:

We propose a criterion for selecting hyperparameters in multicategory support vector machines (MSVM) and apply it to microarray data analysis. The MSVM classifiers are based on a recent extension that inherits the optimal property of the binary SVM that treat all classes simultaneously. The tuning criterion is an extension of the radius-margin bound in binary case that has better smoothness properties than the GACV (generalized approximate cross-validation) criterion and is optimized by a direction set method with respect to both kernel parameters and a balancing parameter between data fit and the complexity of the decision function. Computational examples involving data in public domain are given to illustrate the application of the criterion in gene expression profile classification and gene selection.

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