dc.contributor.author | Evgeniou, Theodoros | en_US |
dc.contributor.author | Pontil, Massimiliano | en_US |
dc.contributor.author | Poggio, Tomaso | en_US |
dc.date.accessioned | 2004-10-20T21:04:32Z | |
dc.date.available | 2004-10-20T21:04:32Z | |
dc.date.issued | 1999-03-01 | en_US |
dc.identifier.other | AIM-1654 | en_US |
dc.identifier.other | CBCL-171 | en_US |
dc.identifier.uri | http://hdl.handle.net/1721.1/7261 | |
dc.description.abstract | Regularization Networks and Support Vector Machines are techniques for solving certain problems of learning from examples -- in particular the regression problem of approximating a multivariate function from sparse data. We present both formulations in a unified framework, namely in the context of Vapnik's theory of statistical learning which provides a general foundation for the learning problem, combining functional analysis and statistics. | en_US |
dc.format.extent | 1526865 bytes | |
dc.format.extent | 959195 bytes | |
dc.format.mimetype | application/postscript | |
dc.format.mimetype | application/pdf | |
dc.language.iso | en_US | |
dc.relation.ispartofseries | AIM-1654 | en_US |
dc.relation.ispartofseries | CBCL-171 | en_US |
dc.title | A Unified Framework for Regularization Networks and Support Vector Machines | en_US |