A Unified Framework for Regularization Networks and Support Vector Machines
| dc.date.accessioned | 2004-10-20T21:04:32Z | |
| dc.date.accessioned | 2018-11-24T10:23:34Z | |
| dc.date.available | 2004-10-20T21:04:32Z | |
| dc.date.available | 2018-11-24T10:23:34Z | |
| dc.date.issued | 1999-03-01 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1721.1/7261 | |
| dc.identifier.uri | http://repository.aust.edu.ng/xmlui/handle/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.language.iso | en_US | |
| dc.title | A Unified Framework for Regularization Networks and Support Vector Machines | en_US |
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