Regularization Networks and Support Vector Machines |
| |
Authors: | Evgeniou Theodoros Pontil Massimiliano Poggio Tomaso |
| |
Institution: | (1) Center for Biological and Computational Learning and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA E-mail: |
| |
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. Radial Basis Functions,
for example, are a special case of both regularization and Support Vector Machines. We review both formulations in the context
of Vapnik's theory of statistical learning which provides a general foundation for the learning problem, combining functional
analysis and statistics. The emphasis is on regression: classification is treated as a special case.
This revised version was published online in June 2006 with corrections to the Cover Date. |
| |
Keywords: | regularization Radial Basis Functions Support Vector Machines Reproducing Kernel Hilbert Space Structural Risk Minimization |
本文献已被 SpringerLink 等数据库收录! |