2009 InformationTheoreticRegularizat

• (Zheng et al., 2009) ⇒ Lei Zheng, Shaojun Wang, Yan Liu, and Chi-Hoon Lee. (2009). “Information Theoretic Regularization for Semi-supervised Boosting.” In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2009). doi:10.1145/1557019.1557129

Subject Headings:

Notes

• Categories and Subject Descriptors: I.2 Artificial Intelligence: Learning.
• General Terms: Algorithms, Experimentation, Performance

Cited By

• http://scholar.google.com/scholar?q=%22Information+theoretic+regularization+for+semi-supervised+boosting%22+2009
• http://portal.acm.org/citation.cfm?doid=1557019.1557129&preflayout=flat#citedby

Quotes

Author Keywords

Ensemble Method, Semi-supervised Learning

Abstract

We present novel semi-supervised boosting algorithms that incrementally build linear combinations of weak classifiers through generic functional gradient descent using both labeled and unlabeled training data. Our approach is based on extending information regularization framework to boosting, bearing loss functions that combine log loss on labeled data with the information-theoretic measures to encode unlabeled data. Even though the information-theoretic regularization terms make the optimization non-convex, we propose simple sequential gradient descent optimization algorithms, and obtain impressively improved results on synthetic, benchmark and real world tasks over supervised boosting algorithms which use the labeled data alone and a state-of-the-art semi-supervised boosting algorithm.

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References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2009 InformationTheoreticRegularizat	Yan Liu Chi-Hoon Lee Lei Zheng Shaojun Wang			Information Theoretic Regularization for Semi-supervised Boosting		KDD-2009 Proceedings		10.1145/1557019.1557129		2009