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 proceeding › 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	Proceedings of The Fifth International Conference on Neural Information Processing, ICONIP 1998
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
Event	5th International Conference on Neural Information Processing, ICONIP 1998 - Kitakyushu, Japan Duration: 21 Oct 1998 → 23 Oct 1998

Conference

Conference	5th International Conference on Neural Information Processing, ICONIP 1998
Country/Territory	Japan
City	Kitakyushu
Period	21/10/98 → 23/10/98

Cite this

@inproceedings{38ea348a28764fe4af13bab311cc2b49,

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}",

year = "1998",

month = oct,

day = "21",

language = "English",

isbn = "4274902595",

pages = "347--351",

editor = "Shiro Usui and Takashi Omori",

booktitle = "Proceedings of The Fifth International Conference on Neural Information Processing, ICONIP 1998",

publisher = "IOA Press",

note = "5th International Conference on Neural Information Processing, ICONIP 1998 ; Conference date: 21-10-1998 Through 23-10-1998",

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T1 - Automated Text Categorization Using Support Vector Machine

AU - Kwok, James Tin-Yau

PY - 1998/10/21

Y1 - 1998/10/21

N2 - 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.

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 proceeding

SN - 4274902595

SN - 9784274902598

SP - 347

EP - 351

BT - Proceedings of The Fifth International Conference on Neural Information Processing, ICONIP 1998

A2 - Usui, Shiro

A2 - Omori, Takashi

PB - IOA Press

T2 - 5th International Conference on Neural Information Processing, ICONIP 1998

Y2 - 21 October 1998 through 23 October 1998

ER -

Automated Text Categorization Using Support Vector Machine

Abstract

Conference

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