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KDD
2009
ACM

Information theoretic regularization for semi-supervised boosting

12 years 5 months ago
Information theoretic regularization for semi-supervised boosting
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 semisupervised boosting algorithm. Categories and Subject Descriptors I.2 [Artificial Intelligence]: Learning General Terms Algorithms, Experimentation, Performance Keywords Ensemble method, semi-supervised learning
Lei Zheng, Shaojun Wang, Yan Liu, Chi-Hoon Lee
Added 25 Nov 2009
Updated 25 Nov 2009
Type Conference
Year 2009
Where KDD
Authors Lei Zheng, Shaojun Wang, Yan Liu, Chi-Hoon Lee
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