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  Semi-Supervised Learning through Principal Directions Estimation

Chapelle, O., Schölkopf, B., & Weston, J. (2003). Semi-Supervised Learning through Principal Directions Estimation. In ICML 2003 Workshop on the Continuum from Labeled to Unlabeled Data in Machine Learning & Data Mining (pp. 1-7).

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-DDB6-8 Version Permalink: http://hdl.handle.net/21.11116/0000-0005-7189-1
Genre: Conference Paper

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ICML-2003-Chapelle_.pdf (Any fulltext), 125KB
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 Creators:
Chapelle, O1, 2, Author              
Schölkopf, B1, 2, Author              
Weston, J1, 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: We describe methods for taking into account unlabeled data in the training of a kernel-based classifier, such as a Support Vector Machines (SVM). We propose two approaches utilizing unlabeled points in the vicinity of labeled ones. Both of the approaches effectively modify the metric of the pattern space, either by using non-spherical Gaussian density estimates which are determined using EM, or by modifying the kernel function using displacement vectors computed from pairs of unlabeled and labeled points. The latter is linked to techniques for training invariant SVMs. We present experimental results indicating that the proposed technique can lead to substantial improvements of classification accuracy.

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 Dates: 2003-08
 Publication Status: Published online
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 Identifiers: BibTex Citekey: 2168
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Title: ICML 2003 Workshop on the Continuum from Labeled to Unlabeled Data in Machine Learning & Data Mining
Place of Event: Washington, DC, USA
Start-/End Date: 2003-08-21

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Title: ICML 2003 Workshop on the Continuum from Labeled to Unlabeled Data in Machine Learning & Data Mining
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1 - 7 Identifier: -