The Strength of Weak Learnability
Machine Learning
Elements of information theory
Elements of information theory
A Classification EM algorithm for clustering and two stochastic versions
Computational Statistics & Data Analysis - Special issue on optimization techniques in statistics
Inducing Features of Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Prediction games and arcing algorithms
Neural Computation
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Logistic Regression, AdaBoost and Bregman Distances
Machine Learning
Exploiting unlabeled data in ensemble methods
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Convex Optimization
Boosting grammatical inference with confidence oracles
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Semi-supervised conditional random fields for improved sequence segmentation and labeling
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Boosting with incomplete information
Proceedings of the 25th international conference on Machine learning
IEEE Transactions on Information Theory - Part 2
Boosting with structure information in the functional space: an application to graph classification
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
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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.