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