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Active learning based on support vector machines

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

Abstract

Active learning is mainly to select a part of unlabelled samples from a big dataset. The selected samples are then submitted to domain experts to label and added to the training set. Suppose that the price of labeling samples is far more than the computational cost of training algorithms, we propose a scheme of active learning based on support vector machines, which follows the traditionally inductive learning model of general-specific. In terms of the number of selected samples, the training cost, and the generalization ability, a comparison with some existing active learning algorithms is conducted. The advantages and disadvantages are demonstrated experimentally. ©2010 IEEE.
Original languageEnglish
Title of host publicationConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Pages1312-1316
DOIs
Publication statusPublished - 2010
Event2010 IEEE International Conference on Systems, Man and Cybernetics, SMC 2010 - Istanbul, Türkiye
Duration: 10 Oct 201013 Oct 2010

Publication series

Name
ISSN (Print)1062-922X

Conference

Conference2010 IEEE International Conference on Systems, Man and Cybernetics, SMC 2010
PlaceTürkiye
CityIstanbul
Period10/10/1013/10/10

Research Keywords

  • Active learning
  • Order in hypothesis space
  • Sample selection
  • SVM

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