Abstract
We propose a generative statistical model for the classification of semi-structured multimedia documents. Its main originality is its ability to simultaneously take into account the structural and the content information present in a semi-structured document and also to cope with different types of content (text, image, etc.). We then present the results obtained on two sets of experiments:
• One set concerns the filtering of pornographic Web pages
• The second one concerns the thematic classification of Wikipedia documents.
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Denoyer, L., Gallinari, P. (2008). Machine Learning for Semi-structured Multimedia Documents: Application to Pornographic Filtering and Thematic Categorization. In: Cord, M., Cunningham, P. (eds) Machine Learning Techniques for Multimedia. Cognitive Technologies. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75171-7_10
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DOI: https://doi.org/10.1007/978-3-540-75171-7_10
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