PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Tighter PAC-Bayes Bounds
Amiran Ambroladze, Emilio Parrado-Hernandez and John Shawe-Taylor
Advances in Neural Information Processing Systems 19 2007.

Abstract

This paper proposes a PAC-Bayes bound to measure the performance of Support Vector Machine (SVM) classifiers. The bound is based on learning a prior over the distribution of classifiers with a part of the training samples. Experimental work shows that this bound is tighter than the original PAC-Bayes, resulting in an enhancement of the predictive capabilities of the PAC-Bayes bound. In addition, it is shown that the use of this bound as a means to estimate the hyperparameters of the classifier compares favourably with cross validation in terms of accuracy of the model, while saving a lot of computational burden.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:2422
Deposited By:Emilio Parrado-Hernandez
Deposited On:22 November 2006