Machine Learning for Semi-Structured Multimedia Documents : Application to pornographic filtering and thematic categorization - Archive ouverte HAL
Book Sections Year : 2008

Machine Learning for Semi-Structured Multimedia Documents : Application to pornographic filtering and thematic categorization

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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Dates and versions

hal-01305052 , version 1 (20-04-2016)

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Ludovic Denoyer, Patrick Gallinari. Machine Learning for Semi-Structured Multimedia Documents : Application to pornographic filtering and thematic categorization. Machine Learning Techniques for Multimedia Content, Springer, pp.227-247, 2008, Cognitive Technologies, 978-3-540-75170-0. ⟨10.1007/978-3-540-75171-7_10⟩. ⟨hal-01305052⟩
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