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  A Taxonomy for Semi-Supervised Learning Methods

Seeger, M. (2006). A Taxonomy for Semi-Supervised Learning Methods. In O. Chapelle, B. Schölkopf, & A. Zien (Eds.), Semi-Supervised Learning (pp. 15-31). Cambridge, MA, USA: MIT Press.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0004-9CE3-C Version Permalink: http://hdl.handle.net/21.11116/0000-0004-9CE4-B
Genre: Book Chapter

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 Creators:
Seeger, M1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: This chapter proposes a simple taxonomy of probabilistic graphical models for the semi-supervised learning (SSL) problem. It provides some broad classes of algorithms for each of the families and points to specific realizations in the literature. Finally, more detailed light is shed on the family of methods using input-dependent regularization or conditional prior distributions, and parallels to the co-training paradigm are shown. The SSL problem has recently attracted the machine learning community, mainly due to its significant importance in practical applications. The chapter then defines the problem and introduces the notation to be used. It is argued here that SSL is much more a practical than a theoretical problem. A useful SSL technique should be configurable to the specifics of the task in a similar way as Bayesian learning, through the choice of prior and model.

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 Dates: 2006
 Publication Status: Published in print
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Title: Semi-Supervised Learning
Source Genre: Book
 Creator(s):
Chapelle, O1, Editor            
Schölkopf, B1, Editor            
Zien, A1, Editor            
Affiliations:
1 Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794            
Publ. Info: Cambridge, MA, USA : MIT Press
Pages: 508 Volume / Issue: - Sequence Number: 1 Start / End Page: 15 - 31 Identifier: ISBN: 0-262-03358-5