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  A Maximum Entropy Approach to Semi-supervised Learning

Erkan, A., & Altun, Y. (2010). A Maximum Entropy Approach to Semi-supervised Learning. Poster presented at 30th International Workshop on Bayesian Inference and Maximun Entropy Methods in Science and Engineering (MaxEnt 2010), Chamonix, France.

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Erkan, AN1, 2, Author              
Altun, Y1, 2, Author              
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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: Maximum entropy (MaxEnt) framework has been studied extensively in supervised learning. Here, the goal is to find a distribution p that maximizes an entropy function while enforcing data constraints so that the expected values of some (pre-defined) features with respect to p match their empirical counterparts approximately. Using different entropy measures, different model spaces for p and different approximation criteria for the data constraints yields a family of discriminative supervised learning methods (e.g., logistic regression, conditional random fields, least squares and boosting). This framework is known as the generalized maximum entropy framework. Semi-supervised learning (SSL) has emerged in the last decade as a promising field that combines unlabeled data along with labeled data so as to increase the accuracy and robustness of inference algorithms. However, most SSL algorithms to date have had trade-offs, e.g., in terms of scalability or applicability to multi-categorical data. We extend the generalized MaxEnt framework to develop a family of novel SSL algorithms. Extensive empirical evaluation on benchmark data sets that are widely used in the literature demonstrates the validity and competitiveness of the proposed algorithms.

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 Dates: 2010-07
 Publication Status: Published in print
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 Identifiers: BibTex Citekey: 6747
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Title: 30th International Workshop on Bayesian Inference and Maximun Entropy Methods in Science and Engineering (MaxEnt 2010)
Place of Event: Chamonix, France
Start-/End Date: 2010-07-04 - 2010-07-09

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Title: 30th International Workshop on Bayesian Inference and Maximun Entropy Methods in Science and Engineering (MaxEnt 2010)
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 80 Identifier: -