Advances in predictive models for data mining

Abstract

Expanding application demand for data mining of massive data warehouses has fueled advances in automated predictive methods. We examine a few successful application areas and their technical challenges. We review the key theoretical developments in PAC and statistical learning theory that have lead to the development of support vector machines and to the use of multiple models for increased predictive accuracy.