Evgeniou, T; Pontil, M; Poggio, T; (2000) Statistical learning theory: A primer. INT J COMPUT VISION , 38 (1) 9 - 13.
Full text not available from this repository.
In this paper we first overview the main concepts of Statistical Learning Theory, a framework in which learning from examples can be studied in a principled way. We then briefly discuss well known as well as emerging learning techniques such as Regularization Networks and Support Vector Machines which can be justified in term of the same induction principle.
|Title:||Statistical learning theory: A primer|
|Keywords:||VC-dimension, structural risk minimization, regularization networks, support vector machines|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science > Computer Science|
Archive Staff Only: edit this record