A passive-aggressive algorithm for semi-supervised learning

Chien Chung Chang*, Yuh-Jye Lee, Hsing Kuo Pao

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Scopus citations

Abstract

In this paper, we proposed a novel semi-supervised learning algorithm, named passive-aggressive semi-supervised learner, which consists of the concepts of passive-aggressive, down-weighting, and multi-view scheme. Our approach performs the labeling and training procedures iteratively. In labeling procedure, we use two views, known as teacher's classifiers for consensus training to obtain a set of guessed labeled points. In training procedure, we use the idea of down-weighting to retrain the third view, i.e., student's classifier by the given initial labeled and guessed labeled points. Based on the idea of passive-aggressive algorithm, we would also like the new retrained classifier to be held as near as possible to the original classifier produced by the initial labeled data. The experiment results showed that our method only uses a small portion of the labeled training data points, but its test accuracy is comparable to the pure supervised learning scheme that uses all the labeled data points for training.

Original languageEnglish
Title of host publicationProceedings - International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2010
Pages335-341
Number of pages7
DOIs
StatePublished - 1 Dec 2010
Event2010 15th Conference on Technologies and Applications of Artificial Intelligence, TAAI 2010 - Hsinchu, Taiwan
Duration: 18 Nov 201020 Nov 2010

Publication series

NameProceedings - International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2010

Conference

Conference2010 15th Conference on Technologies and Applications of Artificial Intelligence, TAAI 2010
Country/TerritoryTaiwan
CityHsinchu
Period18/11/1020/11/10

Keywords

  • Co-training
  • Consensus training
  • Down-weighting
  • Incremental reduced support vector machine
  • Multi-view
  • Passive-aggressive
  • Reduced set

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