Incremental support vector machine for unlabeled data classification

Jin Hyuk Hong, Sung Bae Cho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 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 languageEnglish
Title of host publicationICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing
Subtitle of host publicationComputational Intelligence for the E-Age
EditorsJagath C. Rajapakse, Soo-Young Lee, Lipo Wang, Kunihiko Fukushima, Xin Yao
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1403-1407
Number of pages5
ISBN (Electronic)9810475241, 9789810475246
DOIs
Publication statusPublished - 2002 Jan 1
Event9th International Conference on Neural Information Processing, ICONIP 2002 - Singapore, Singapore
Duration: 2002 Nov 182002 Nov 22

Publication series

NameICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age
Volume3

Other

Other9th International Conference on Neural Information Processing, ICONIP 2002
CountrySingapore
CitySingapore
Period02/11/1802/11/22

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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  • 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). [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