Results on learnability and the Vapnik-Chervonenkis dimension
Information and Computation
C4.5: programs for machine learning
C4.5: programs for machine learning
The weighted majority algorithm
Information and Computation
An experimental and theoretical comparison of model selection methods
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
Predicting Nearly As Well As the Best Pruning of a Decision Tree
Machine Learning - Special issue on the eighth annual conference on computational learning theory, (COLT '95)
Journal of the ACM (JACM)
Using and combining predictors that specialize
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
A PAC analysis of a Bayesian estimator
COLT '97 Proceedings of the tenth annual conference on Computational learning theory
An efficient extension to mixture techniques for prediction and decision trees
COLT '97 Proceedings of the tenth annual conference on Computational learning theory
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
A Fast, Bottom-Up Decision Tree Pruning Algorithm with Near-Optimal Generalization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Concept learning using complexity regularization
IEEE Transactions on Information Theory
Pac-bayesian generalisation error bounds for gaussian process classification
The Journal of Machine Learning Research
Generalization error bounds for Bayesian mixture algorithms
The Journal of Machine Learning Research
Semi-supervised learning using randomized mincuts
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A comparison of tight generalization error bounds
ICML '05 Proceedings of the 22nd international conference on Machine learning
PAC-Bayes risk bounds for sample-compressed Gibbs classifiers
ICML '05 Proceedings of the 22nd international conference on Machine learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
PAC-Bayesian learning of linear classifiers
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Explicit learning curves for transduction and application to clustering and compression algorithms
Journal of Artificial Intelligence Research
Transductive Rademacher complexity and its applications
Journal of Artificial Intelligence Research
Learning Permutations with Exponential Weights
The Journal of Machine Learning Research
COLT'07 Proceedings of the 20th annual conference on Learning theory
Learning permutations with exponential weights
COLT'07 Proceedings of the 20th annual conference on Learning theory
Learning with randomized majority votes
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Human Action Segmentation and Recognition Using Discriminative Semi-Markov Models
International Journal of Computer Vision
The missing consistency theorem for bayesian learning: stochastic model selection
ALT'06 Proceedings of the 17th international conference on Algorithmic Learning Theory
Generalization error bounds using unlabeled data
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Sparse regression learning by aggregation and Langevin Monte-Carlo
Journal of Computer and System Sciences
The safe bayesian: learning the learning rate via the mixability gap
ALT'12 Proceedings of the 23rd international conference on Algorithmic Learning Theory
A Computational Learning Theory of Active Object Recognition Under Uncertainty
International Journal of Computer Vision
Scalable inference in max-margin topic models
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Generalized relational topic models with data augmentation
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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PAC-Bayesian learning methods combine the informative priors of Bayesian methods with distribution-free PAC guarantees. Stochastic model selection predicts a class label by stochastically sampling a classifier according to a “posterior distribution” on classifiers. This paper gives a PAC-Bayesian performance guarantee for stochastic model selection that is superior to analogous guarantees for deterministic model selection. The guarantee is stated in terms of the training error of the stochastic classifier and the KL-divergence of the posterior from the prior. It is shown that the posterior optimizing the performance guarantee is a Gibbs distribution. Simpler posterior distributions are also derived that have nearly optimal performance guarantees.