A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Covering number bounds of certain regularized linear function classes
The Journal of Machine Learning Research
Pac-bayesian generalisation error bounds for gaussian process classification
The Journal of Machine Learning Research
Rademacher and gaussian complexities: risk bounds and structural results
The Journal of Machine Learning Research
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Tutorial on Practical Prediction Theory for Classification
The Journal of Machine Learning Research
PAC-Bayesian learning of linear classifiers
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Structural risk minimization over data-dependent hierarchies
IEEE Transactions on Information Theory
Engineering Applications of Artificial Intelligence
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This paper presents the prior PAC-Bayes bound and explores its capabilities as a tool to provide tight predictions of SVMs' generalization. The computation of the bound involves estimating a prior of the distribution of classifiers from the available data, and then manipulating this prior in the usual PAC-Bayes generalization bound. We explore two alternatives: to learn the prior from a separate data set, or to consider an expectation prior that does not need this separate data set. The prior PAC-Bayes bound motivates two SVM-like classification algorithms, prior SVM and ν-prior SVM, whose regularization term pushes towards the minimization of the prior PAC-Bayes bound. The experimental work illustrates that the new bounds can be significantly tighter than the original PAC-Bayes bound when applied to SVMs, and among them the combination of the prior PAC-Bayes bound and the prior SVM algorithm gives the tightest bound.