Efficient distribution-free learning of probabilistic concepts
Proceedings of a workshop on Computational learning theory and natural learning systems (vol. 1) : constraints and prospects: constraints and prospects
Learning to cluster using local neighborhood structure
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Estimating and computing density based distance metrics
ICML '05 Proceedings of the 22nd international conference on Machine learning
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
The asymptotics of semi-supervised learning in discriminative probabilistic models
Proceedings of the 25th international conference on Machine learning
Discriminatively regularized least-squares classification
Pattern Recognition
Fast semi-supervised SVM classifiers using a priori metric information
Optimization Methods & Software - Mathematical programming in data mining and machine learning
Regularization and feature selection in least-squares temporal difference learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Multi-conditional learning: generative/discriminative training for clustering and classification
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Soft-supervised learning for text classification
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Query Selection via Weighted Entropy in Graph-Based Semi-supervised Classification
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
Generalized Expectation Criteria for Semi-Supervised Learning with Weakly Labeled Data
The Journal of Machine Learning Research
Efficient graph-based semi-supervised learning of structured tagging models
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Semi-Supervised Learning with Measure Propagation
The Journal of Machine Learning Research
Graph-based lexicon expansion with sparsity-inducing penalties
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
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We formulate a principle for classification with the knowledge of the marginal distribution over the data points (unlabeled data). The principle is cast in terms of Tikhonov style regularization where the regularization penalty articulates the way in which the marginal density should constrain otherwise unrestricted conditional distributions. Specifically, the regularization penalty penalizes any information introduced between the examples and labels beyond what is provided by the available labeled examples. The work extends (Szummer and Jaakkola, 2003) to multiple dimensions, providing a regularizer independent of the covering of the space used in the derivation. In addition we lay the learning theoretical framework for classification with information regularization and provide a sample complexity bound. We illustrate the regularization principle in practice by restricting the class of conditional distributions to be logistic regression models and constructing the regularization penalty from a finite set of unlabeled examples.