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A Unified Framework for Regularization Networks and Support Vector Machines
| Content Provider | Semantic Scholar |
|---|---|
| Author | Evgeniou, Theodoros Pontil, Massimiliano Poggio, Tomaso |
| Copyright Year | 1999 |
| 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. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.ai.mit.edu/projects/cbcl/publications/ps/reviewall-memoreview.ps |
| Alternate Webpage(s) | http://dspace.mit.edu/bitstream/handle/1721.1/7261/AIM-1654.pdf?sequence=2 |
| Alternate Webpage(s) | http://www.svms.org/regularization/EVPP99a.pdf |
| Alternate Webpage(s) | http://svms.org/regularization/EVPP99a.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
