GE-CKO: A Method to Optimize Composite Kernels for Web Page Classification

Jiantao Sun, Benyu Zhang, Zheng Chen, Yuchang Lu, Cuiyi Shi, and Wei-Ying Ma

Abstract

Most of current researches on Web page classification focus on leveraging heterogeneous features such as plain text, hyperlinks and anchor texts in an effective and efficient way. Composite kernel method is one topic of interest among them. It first selects a bunch of initial kernels, each of which is determined separately by a certain type of features. Then a classifier is trained based on a linear combination of these kernels. In this paper, we propose an effective way to optimize the linear combination of kernels. We proved that this problem is equivalent to solving a generalized eigenvalue problem. And the weight vector of the kernels is the eigenvector associated with the largest eigen-value. A support vector machine (SVM) classifier is then trained based on this optimized combination of kernels. Our experiment on the WebKB dataset has shown the effectiveness of our proposed method.

Details

Publication typeInproceedings
Published inIEEE/WIC International Conference on Web Intelligence 2004
PublisherIEEE Computer Society
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