Combining content and structure similarity for XML document classification using composite SVM kernels

Combination of structure and content features is necessary for effective retrieval and classification of XML documents. Composite kernels provide a way for fusion of content and structure information. In this paper, we demonstrate that a linear combination of simple and low cost kernels such as cosine similarity on terms and selective paths provide a good classification performance. We also propose a corpus-driven entropybased heuristic for determining the optimal combination weights. Classification experiments performed on

• Type: Conference
• Year: 2008
• Where: ICPR
• Authors: Pabitra Mitra, Saptarshi Ghosh
• Keywords: Computer Vision, ICPR 2008, Linear Combination, Low Cost Kernels, Optimal Combination Weights