Please use this identifier to cite or link to this item:
http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/7261| Title: | A Unified Framework for Regularization Networks and Support Vector Machines |
| Issue Date: | 9-Oct-2013 |
| Description: | 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. |
| URI: | http://koha.mediu.edu.my:8181/xmlui/handle/1721 |
| Other Identifiers: | AIM-1654 CBCL-171 http://hdl.handle.net/1721.1/7261 |
| Appears in Collections: | MIT Items |
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