Drezet, P. and Harrison, R.F. (1998) Directly Optimised Support Vector Machines for Classification and Regression. Research Report. ACSE Research Report 715 . Department of Automatic Control and Systems Engineering
Abstract
A new method of implementing Support Vector learning algorithms for classification and regression is presented which deals with problems of over-defined solutions and excessive complexity. Classification problems are solved with the minimum number of support vectors, irrespective of over-lapping training data. Support vector regression can be solved as a sparse solution, without requiring an e-insensitive zone. The optimisation method is generalised to include control of sparsity for both support vector classification and regression.
Metadata
Item Type: | Monograph |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | The Department of Automatic Control and Systems Engineering research reports offer a forum for the research output of the academic staff and research students of the Department at the University of Sheffield. Papers are reviewed for quality and presentation by a departmental editor. However, the contents and opinions expressed remain the responsibility of the authors. Some papers in the series may have been subsequently published elsewhere and you are advised to cite the later published version in these instances. |
Keywords: | Support Vector Machines, Support Vector Regression, Sparse Approximation. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) > ACSE Research Reports |
Depositing User: | MRS ALISON THERESA BARNETT |
Date Deposited: | 19 Nov 2014 11:32 |
Last Modified: | 29 Mar 2018 18:26 |
Status: | Published |
Publisher: | Department of Automatic Control and Systems Engineering |
Series Name: | ACSE Research Report 715 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:81790 |