Learning the Prior for the PAC-Bayes Bound
Amiran Ambroladze, Emilio Parrado-Hernandez and John Shawe-Taylor
Unpublished, Southampton, UK.
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 classi ers 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.