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Multi-class SVM with Negative Data Selection for Web Page Classification

Multi-class SVM with Negative Data Selection for Web Page Classification. Chih-Ming Chen, Hahn-Ming Lee and Ming-Tyan Kao International Joint Conference on Neural Networks 2004. Motivation. Several new websites are launched everyday Need to search fast and efficiently

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Multi-class SVM with Negative Data Selection for Web Page Classification

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  1. Multi-class SVM with Negative Data Selection for Web Page Classification Chih-Ming Chen, Hahn-Ming Lee and Ming-Tyan Kao International Joint Conference on Neural Networks 2004

  2. Motivation • Several new websites are launched everyday • Need to search fast and efficiently • Search engines organize websites under topic hierarchy (taxonomy) • Need a classifier: one-against-all SVM • Catch: huge negative data increased training time

  3. Negative Data Selection Support vectors in the negative data are much similar to the positive data than the other negative data

  4. Negative Data Selection • Feature Selection: top n keywords from the positive data • All websites are represented as vectors of these top n keywords. • Cosine Similarity:

  5. Negative Data Selection • Plot similarity scores of negative to positive documents in descending order with negative documents Convergence Point Similarity Scores in Descending order Negative Documents

  6. Experiments • Reuters dataset (10802 training, 565 test)

  7. Experiments

  8. Experiments

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