Frieb, Thilo-Thomas and Harrison, R. (1998) Support Vector Neural Networks. Research Report. ACSE Research Report 725 . Department of Automatic Control and Systems Engineering
Abstract
The kernel Adatron support vector neural network (SVNN) is a new neural network alternative to support vector (SV) machines. It can learn large-margin decision functions in kernel feature spaces in an iterative "on-line" fashion which are IDENTICAL to support vector machines. In contrast "conventional" support vector learning is batch learning and is strongly based on solving constrained quadratic programming problems. Quadratic programming is nontrivial to implement and can be subject to stability problems..........
Metadata
Authors/Creators: |
|
---|---|
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: | Neural Networks, Peceptron, Adaton, Kernel Functions, Method of Potential Functions, Statistical Mechanics, Support Vector Learning. |
Dates: |
|
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: | 03 Mar 2015 10:35 |
Last Modified: | 29 Mar 2018 04:59 |
Status: | Published |
Publisher: | Department of Automatic Control and Systems Engineering |
Series Name: | ACSE Research Report 725 |