In this paper, Deep Belief Network(DBN) is used for drug-related webpages classification. HTML parsing is used to extract image-label text and body text, FOCARSS method is used to choose effective images. text representation is generated by BOW model, images representation is generated by BOF model. We concatenate images and text representation to generate final representation. It is shown that DBN’s classification accuracy is higher than BPNN’s classification accuracy, and better than that of single-modal information.


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    Title :

    Webpages Classification Based on Deep Belief Network Using Images and Text Information


    Contributors:
    Hu, Ruiguang (author) / Gao, Shibo (author) / Yang, Libo (author)


    Publication date :

    2018-08-01


    Size :

    422614 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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