教師資料查詢 | 類別: 期刊論文 | 教師: 林慧珍 Lin Hwei-jen (瀏覽個人網頁)

標題: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
語言英文
ISSN0096-3003
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
國別美國
公開徵稿
出版型式紙本
相關連結
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