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Learning SVM Classiers with Indenite Kernels Suicheng GuandYuhong Guo Department of Computer and Information Sciences Temple University Philadelphia, PA 19122, USA yuhong@temple.edu Abstract Recently, training support vector machines with indef- inite kernels has attracted great attention in the ma- chine learning community. In this paper, we tackle this problem by formulating a joint optimization model over SVM classications and kernel principal compo- nent analysis. We rst reformulate the kernel principal component analysis as a general kernel transformation framework, and then incorporate it into the SVM clas-
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Author:
Suicheng Gu, Yuhong Guo
CreationDate:
2012-05-08T12:17:53-07:00
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Keywords:
Machine Learning (Main Track)
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2012-07-11T15:07:37-07:00
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Subject:
Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence
Title:
Learning SVM Classifiers with Indefinite Kernels
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Dc:creator:
Suicheng Gu, Yuhong Guo
Dc:title:
Learning SVM Classifiers with Indefinite Kernels
Dc:description:
Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence
Dc:subject:
Machine Learning (Main Track)
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Copyright © 2012 Association for the Advancement of Artificial Intelligence. All rights reserved.
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