Incremental support vector machine for unlabeled data classification

Jin Hyuk Hong; Sung Bae Cho

doi:10.1109/ICONIP.2002.1202851

Incremental support vector machine for unlabeled data classification

Jin Hyuk Hong, Sung Bae Cho

College of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

11 Citations (Scopus)

Abstract

Due to the wide proliferation of the Internet and telecommunication, huge amount of information has been produced as digital data format. It is impossible to classify this information with one's own hand one by one in many realistic problems, so that the research on automatic text classification has been grown. Machine learning technologies have applied in text classification. However, the traditional statistic machine learning technologies require large number of labeled training examples to learn accurately. To obtain enough training examples, we have to label on these huge training examples by hand. This paper presents a supervised learning algorithm based on support vector machine (SVM) to classify text documents more accurately by using unlabeled documents to augment available labeled training examples. Experimental results indicate that the classification with unlabeled examples using SVM is superior to the conventional classification,with labeled examples.

Original language	English
Title of host publication	ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing
Subtitle of host publication	Computational Intelligence for the E-Age
Editors	Jagath C. Rajapakse, Soo-Young Lee, Lipo Wang, Kunihiko Fukushima, Xin Yao
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1403-1407
Number of pages	5
ISBN (Electronic)	9810475241, 9789810475246
DOIs	https://doi.org/10.1109/ICONIP.2002.1202851
Publication status	Published - 2002
Event	9th International Conference on Neural Information Processing, ICONIP 2002 - Singapore, Singapore Duration: 2002 Nov 18 → 2002 Nov 22

Publication series

Name	ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age
Volume	3

Other

Other	9th International Conference on Neural Information Processing, ICONIP 2002
Country/Territory	Singapore
City	Singapore
Period	02/11/18 → 02/11/22

Bibliographical note

Funding Information:
This paper was supported by Brain Science and Engineering Research program sponsored by Korean Ministry of Science and Technology.

Publisher Copyright:
© 2002 Nanyang Technological University.

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Information Systems
Signal Processing

Access to Document

10.1109/ICONIP.2002.1202851

Cite this

Hong, J. H., & Cho, S. B. (2002). Incremental support vector machine for unlabeled data classification. In J. C. Rajapakse, S-Y. Lee, L. Wang, K. Fukushima, & X. Yao (Eds.), ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age (pp. 1403-1407). Article 1202851 (ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age; Vol. 3). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICONIP.2002.1202851

Hong, Jin Hyuk ; Cho, Sung Bae. / Incremental support vector machine for unlabeled data classification. ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age. editor / Jagath C. Rajapakse ; Soo-Young Lee ; Lipo Wang ; Kunihiko Fukushima ; Xin Yao. Institute of Electrical and Electronics Engineers Inc., 2002. pp. 1403-1407 (ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age).

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abstract = "Due to the wide proliferation of the Internet and telecommunication, huge amount of information has been produced as digital data format. It is impossible to classify this information with one's own hand one by one in many realistic problems, so that the research on automatic text classification has been grown. Machine learning technologies have applied in text classification. However, the traditional statistic machine learning technologies require large number of labeled training examples to learn accurately. To obtain enough training examples, we have to label on these huge training examples by hand. This paper presents a supervised learning algorithm based on support vector machine (SVM) to classify text documents more accurately by using unlabeled documents to augment available labeled training examples. Experimental results indicate that the classification with unlabeled examples using SVM is superior to the conventional classification,with labeled examples.",

author = "Hong, {Jin Hyuk} and Cho, {Sung Bae}",

note = "Funding Information: This paper was supported by Brain Science and Engineering Research program sponsored by Korean Ministry of Science and Technology. Publisher Copyright: {\textcopyright} 2002 Nanyang Technological University.; 9th International Conference on Neural Information Processing, ICONIP 2002 ; Conference date: 18-11-2002 Through 22-11-2002",

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doi = "10.1109/ICONIP.2002.1202851",

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publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Hong, JH & Cho, SB 2002, Incremental support vector machine for unlabeled data classification. in JC Rajapakse, S-Y Lee, L Wang, K Fukushima & X Yao (eds), ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age., 1202851, ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age, vol. 3, Institute of Electrical and Electronics Engineers Inc., pp. 1403-1407, 9th International Conference on Neural Information Processing, ICONIP 2002, Singapore, Singapore, 02/11/18. https://doi.org/10.1109/ICONIP.2002.1202851

Incremental support vector machine for unlabeled data classification. / Hong, Jin Hyuk; Cho, Sung Bae.
ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age. ed. / Jagath C. Rajapakse; Soo-Young Lee; Lipo Wang; Kunihiko Fukushima; Xin Yao. Institute of Electrical and Electronics Engineers Inc., 2002. p. 1403-1407 1202851 (ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age; Vol. 3).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N2 - Due to the wide proliferation of the Internet and telecommunication, huge amount of information has been produced as digital data format. It is impossible to classify this information with one's own hand one by one in many realistic problems, so that the research on automatic text classification has been grown. Machine learning technologies have applied in text classification. However, the traditional statistic machine learning technologies require large number of labeled training examples to learn accurately. To obtain enough training examples, we have to label on these huge training examples by hand. This paper presents a supervised learning algorithm based on support vector machine (SVM) to classify text documents more accurately by using unlabeled documents to augment available labeled training examples. Experimental results indicate that the classification with unlabeled examples using SVM is superior to the conventional classification,with labeled examples.

AB - Due to the wide proliferation of the Internet and telecommunication, huge amount of information has been produced as digital data format. It is impossible to classify this information with one's own hand one by one in many realistic problems, so that the research on automatic text classification has been grown. Machine learning technologies have applied in text classification. However, the traditional statistic machine learning technologies require large number of labeled training examples to learn accurately. To obtain enough training examples, we have to label on these huge training examples by hand. This paper presents a supervised learning algorithm based on support vector machine (SVM) to classify text documents more accurately by using unlabeled documents to augment available labeled training examples. Experimental results indicate that the classification with unlabeled examples using SVM is superior to the conventional classification,with labeled examples.

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ER -

Hong JH, Cho SB. Incremental support vector machine for unlabeled data classification. In Rajapakse JC, Lee S-Y, Wang L, Fukushima K, Yao X, editors, ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age. Institute of Electrical and Electronics Engineers Inc. 2002. p. 1403-1407. 1202851. (ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age). doi: 10.1109/ICONIP.2002.1202851

Incremental support vector machine for unlabeled data classification

Abstract

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Bibliographical note

All Science Journal Classification (ASJC) codes

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