LABEL-BASED MULTIPLE KERNEL LEARNING FOR CLASSIFICATION

摘要：

　　This paper provides a novel technique for multiple kernel learning within Support Vector Machine framework.The problem of combining different sources of information arises in several situations,for instance,the classification of data with asymmetric similarity matrices or the construction of an optimal classifier from a collection of kernels.Often,each source of information can be expressed as a similarity matrix.In this paper we propose a new method in order to produce a single optimal kernel matrix from a collection of kernel (similarity) matrices with the label information for classification purposes.Then,the constructed kernel matrix is used to train a Support Vector Machine.The key ideas within the kernel construction are twofold: the quantification,relative to the classification labels,of the difference of information among the similarities; and the linear combination of similarity matrices to the concept of functional combination of similarity matrices.The proposed method has been successfully evaluated and compared with other powerful classifiers on a variety of real classification problems.

关键词： Kernel methods Multiple kernel learning Similarity-based classification Support Vector Machine

作者: Bing Yang Qian Li Lujia Song Changhe Fu Ling Jing

作者单位: College of Science, China Agricultural University, Beijing 100083, P.R. China

会议类型: 国际会议

会议名称: 11th International Symposium on Operations Research and its Applications(第11届运筹学及其应用国际研讨会)

会议地点: 安徽黄山

会议语种:英文

页码: 148-152

在线出版日期: 2013-08-23（万方平台首次上网日期，不代表论文的发表时间）