PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Learning the prior for the PAC-Bayes bound
Amiran Ambroladze, Emilio Parrado-Hernandez and John Shawe-Taylor
(2004) Technical Report. Unpublished, Southampton, UK.

Abstract

This paper presents a bound on the performance of a Support Vector Machine obtained within the PAC-Bayes framework. The bound is computed by means of the estimation of a prior of the distribution of SVM classifiers given a particular dataset, and the use of this prior in the PAC-Bayes generalisation bound. The quality of the bound is tested in a model selection task, where it is compared against other procedures to select models based on other PAC-Bayes bounds and ten fold cross-validation. Furthermore, we introduce an algorithm to approximately optimise the new bound and test it against a standard SVM both in terms of bound value and test set error.

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EPrint Type:Monograph (Technical Report)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:301
Deposited By:Emilio Parrado-Hernandez
Deposited On:02 December 2004