publication . Article . 2004

Semisupervised learning of classifiers: theory, algorithms, and their application to human-computer interaction

Fabio Gagliardi Cozman;
Open Access
  • Published: 19 Oct 2004 Journal: IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 26, pages 1,553-1,566 (issn: 0162-8828, Copyright policy)
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Abstract
Automatic classification is one of the basic tasks required in any pattern recognition and human computer interaction application. In this paper, we discuss training probabilistic classifiers with labeled and unlabeled data. We provide a new analysis that shows under what conditions unlabeled data can be used in learning to improve classification performance. We also show that, if the conditions are violated, using unlabeled data can be detrimental to classification performance. We discuss the implications of this analysis to a specific type of probabilistic classifiers, Bayesian networks, and propose a new structure learning algorithm that can utilize unlabeled...
Subjects
ACM Computing Classification System: ComputingMethodologies_PATTERNRECOGNITION
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