Text Categorization Using Informative Vector Machines

摘要

In this paper an analysis is given of the application of Bayesian Gaussian Process statistical learning algorithms to the problem of text categorization. It is demonstrated that the Informative Vector Machine method, as a sparse Bayesian compression scheme, provides results better than those obtained so far with the Support Vector Machine method, with much less computational cost.