document: Web page classification with heterogeneous data fusion Web pages are more than text and they contain much contextual and structural information , e.g. , the title , the meta data , the anchor text , etc. , each of which can be seen as a data source or are presentation . Due to the different dimensionality and different representing forms of these heterogeneous data sources , simply putting them together would not greatly enhance the classification performance . We observe that via a kernel function , different dimensions and types of data sources can be represented into acommon format of kernel matrix , which can be seen as a generalized similarity measure between a pair of web pages . In this sense , a kernel learning approach is employed to fuse these heterogeneous data sources . The experimental results on a collection of the ODP database validate the advantages of the proposed method over traditional methods based on any single data source and the uniformly weighted combination of them .
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