Automated Text Categorization Using Support Vector Machine

James Tin-Yau Kwok

Automated Text Categorization Using Support Vector Machine

James Tin-Yau Kwok

Research output: Chapter in book/report/conference proceeding › Conference contribution › peer-review

Abstract

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 language	English
Title of host publication	The Fifth International Conference on Neural Information Processing, ICONIP'R98, Kitakyushu, Japan, October 21-23, 1998, Proceedings
Editors	Shiro Usui, Takashi Omori
Publisher	IOA Press
Pages	347-351
Number of pages	5
ISBN (Print)	4274902595, 9784274902598
Publication status	Published - 21 Oct 1998
Externally published	Yes

Cite this

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title = "Automated Text Categorization Using Support Vector Machine",

abstract = "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.",

author = "Kwok, {James Tin-Yau}",

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language = "English",

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Automated Text Categorization Using Support Vector Machine. / Kwok, James Tin-Yau.

The Fifth International Conference on Neural Information Processing, ICONIP'R98, Kitakyushu, Japan, October 21-23, 1998, Proceedings. ed. / Shiro Usui; Takashi Omori. IOA Press, 1998. p. 347-351.

Research output: Chapter in book/report/conference proceeding › Conference contribution › peer-review

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AB - 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.

M3 - Conference contribution

SN - 4274902595

SN - 9784274902598

SP - 347

EP - 351

BT - The Fifth International Conference on Neural Information Processing, ICONIP'R98, Kitakyushu, Japan, October 21-23, 1998, Proceedings

A2 - Usui, Shiro

A2 - Omori, Takashi

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ER -

Automated Text Categorization Using Support Vector Machine

Abstract

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