Fast linear stationary methods for Automatically Biased Support Vector Machines
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Lai, Daniel 
ORCID: https://orcid.org/0000-0003-3459-7709, Palaniswami, M and Mani, N
(2003)
Fast linear stationary methods for Automatically Biased Support Vector Machines.
In:
Proceedings of the International Joint Conference on Neural Networks 2003.
IEEE, Piscataway, pp. 2060-2065.
Abstract
We present a new training algorithm, which is capable of providing Fast training for a new automatically biased SVM. We compare our agorithm to the well-known Sequential Minimal Optimization (SMO) algorithm. We then show that this method allows for the application of acceleration methods which further increases the rates of convergence.
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| Additional Information | Doubletree Hotel - Jantzen Beach, Portland, Oregon, July 20-24, 2003 |
| Item type | Book Section |
| URI | https://vuir.vu.edu.au/id/eprint/5714 |
| DOI | 10.1109/IJCNN.2003.1223725 |
| Official URL | http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arn... |
| ISBN | 9780780378988 |
| Subjects | Historical > FOR Classification > 0102 Applied Mathematics Historical > FOR Classification > 0801 Artificial Intelligence and Image Processing Historical > FOR Classification > 1702 Cognitive Science Historical > Faculty/School/Research Centre/Department > Centre for Ageing, Rehabilitation, Exercise & Sport Science (CARES) |
| Keywords | ResPubID19082, machine learning, matrix representation, optimization, pattern recognition, non-linear mapping, hyperplane, extrapolation method, convergence, acceleration |
| Citations in Scopus | 5 - View on Scopus |
| Download/View statistics | View download statistics for this item |
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