Automated Text Categorization Using Support Vector Machine

James Tin-Yau Kwok

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review


In this paper, we study the use of support vector machine in text categorization. Unlike other machine learning techniques, it allows easy incorporation of new documents into an existing trained system. Moreover, dimension reduction, which is usually imperative, now becomes optional. Thus, SVM adapts efficiently in dynamic environments that require frequent additions to the document collection. Empirical results on the Reuters-22173 collection are also discussed.
Original languageEnglish
Title of host publicationThe Fifth International Conference on Neural Information Processing, ICONIP'R98, Kitakyushu, Japan, October 21-23, 1998, Proceedings
EditorsShiro Usui, Takashi Omori
PublisherIOA Press
Number of pages5
ISBN (Print)4274902595, 9784274902598
Publication statusPublished - 21 Oct 1998
Externally publishedYes


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