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A generalized cluster centroid based classifier for text categorization

A generalized cluster centroid based classifier for text categorization. Presenter : Bei -YI Jiang Authors : Guansong Pang, Shengyi Jiang 2013. Information Processing and Management. Outlines. Motivation Objectives Methodology Experiments Conclusions Comments. Motivation. KNN

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A generalized cluster centroid based classifier for text categorization

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  1. A generalized cluster centroid based classifier for text categorization Presenter : Bei-YI JiangAuthors : Guansong Pang, Shengyi Jiang2013. Information Processing and Management

  2. Outlines • Motivation • Objectives • Methodology • Experiments • Conclusions • Comments

  3. Motivation • KNN • With the exponential growth of online textual information, how to organize text data effectively and efficiently has become an important and demanding issue. • Rocchio • Fails to obtain an expressive categorization model due to its inherent linear separability assumption.

  4. Objectives • To strengthen the expressiveness of the Rocchio model. • Employ the improved Rocchio model to speed up the categorization process of KNN.

  5. Methodology • KNN • Rocchio

  6. Methodology

  7. Methodology

  8. Methodology • GCC • Determine the threshold

  9. Experiments

  10. Experiments

  11. Experiments

  12. Experiments

  13. Experiments

  14. Experiments

  15. Experiments

  16. Conclusions • strengthen the expressivenessof the Rocchio model • GCCC and its variants achieve impressive performance • obtain near linear time complexity in modeling • GCCC’s modeling stage is more time-consuming

  17. Comments • Advantages • relatively stable • favorable performance • Applications • online categorization

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