GE-CKO: A Method to Optimize Composite Kernels for Web Page Classification
GE-CKO: A Method to Optimize Composite Kernels for Web Page Classification
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GE-CKO：一种优化网页分类复合内核的方法
DOI：
10.1109/wi.2004.74
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发表时间：
2004
期刊：
影响因子：
--
通讯作者：
Wei
中科院分区：
文献类型：
--
作者：
Jian;Benyu Zhang;Zheng Chen;Yuchang Lu;Chunyi Shi;Wei
当前的网页分类研究主要集中在如何有效地利用纯文本、超链接和锚文本等异质特征。复合核方法是其中的一个研究热点。它首先选择一堆初始内核，每个内核由特定类型的特征单独确定。然后，基于这些内核的线性组合来训练分类器。在本文中，我们提出了一种有效的方法来优化核的线性组合。证明了该问题等价于求解广义特征值问题。核的权向量是与最大特征值相关联的特征向量。然后，基于这种优化的内核组合来训练支持向量机（SVM）分类器。在WebKB数据集上的实验表明了该方法的有效性。
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.