Tighter PAC-Bayes Bounds

Part of Advances in Neural Information Processing Systems 19 (NIPS 2006)

Bibtex Metadata Paper


Amiran Ambroladze, Emilio Parrado-hernández, John Shawe-taylor


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.