Optimal reduction of solutions for support vector machines
學年 98
學期 1
出版(發表)日期 2009-08-01
作品名稱 Optimal reduction of solutions for support vector machines
作品名稱(其他語言)
著者 Lin, Hwei-Jen; Yeh, Jih-Pin
單位 淡江大學資訊工程學系
出版者 Philadelphia: Elsevier Inc.
著錄名稱、卷期、頁數 Applied Mathematics and Computation 214(2), pp.329-335
摘要 Being a universal learning machine, a support vector machine (SVM) suffers from expensive computational cost in the test phase due to the large number of support vectors, and greatly impacts its practical use. To address this problem, we proposed an adaptive genetic algorithm to optimally reduce the solutions for an SVM by selecting vectors from the trained support vector solutions, such that the selected vectors best approximate the original discriminant function. Our method can be applied to SVMs using any general kernel. The size of the reduced set can be used adaptively based on the requirement of the tasks. As such the generalization/complexity trade-off can be controlled directly. The lower bound of the number of selected vectors required to recover the original discriminant function can also be determined.
關鍵字 Support vector machine;Vector correlation;Genetic algorithms;Optimal solution;Discriminant function;Pattern recognition
語言 en
ISSN 0096-3003
期刊性質 國外
收錄於 SCI EI
產學合作
通訊作者 Yeh, Jih-Pin
審稿制度
國別 USA
公開徵稿
出版型式 紙本
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