Mathematics – Statistics Theory
Scientific paper
2008-04-03
Annals of Statistics 2008, Vol. 36, No. 2, 489-531
Mathematics
Statistics Theory
Published in at http://dx.doi.org/10.1214/009053607000000839 the Annals of Statistics (http://www.imstat.org/aos/) by the Inst
Scientific paper
10.1214/009053607000000839
The support vector machine (SVM) algorithm is well known to the computer learning community for its very good practical results. The goal of the present paper is to study this algorithm from a statistical perspective, using tools of concentration theory and empirical processes. Our main result builds on the observation made by other authors that the SVM can be viewed as a statistical regularization procedure. From this point of view, it can also be interpreted as a model selection principle using a penalized criterion. It is then possible to adapt general methods related to model selection in this framework to study two important points: (1) what is the minimum penalty and how does it compare to the penalty actually used in the SVM algorithm; (2) is it possible to obtain ``oracle inequalities'' in that setting, for the specific loss function used in the SVM algorithm? We show that the answer to the latter question is positive and provides relevant insight to the former. Our result shows that it is possible to obtain fast rates of convergence for SVMs.
Blanchard Gilles
Bousquet Olivier
Massart Pascal
No associations
LandOfFree
Statistical performance of support vector machines does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Statistical performance of support vector machines, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Statistical performance of support vector machines will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-352531