Fast linear stationary methods for Automatically Biased Support Vector Machines

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Lai, Daniel ORCID: 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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Doubletree Hotel - Jantzen Beach, Portland, Oregon, July 20-24, 2003

Item type Book Section
URI https://vuir.vu.edu.au/id/eprint/5714
DOI https://doi.org/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
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