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A new momentum minimization decomposition method for support vector machines

Lai, Daniel, Palaniswami, Marimuthu and Mani, N (2004) A new momentum minimization decomposition method for support vector machines. In: Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on. IEEE, pp. 2001-2006.

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Abstract

The Support Vector Machine classifier is a binary classifier applied to classify large datasets, which is ideal for the application of decomposition methods when processing memory is limited. However, the rates of convergence of the decomposition method are largely dependent on the sequence of decomposed problems solved. Unfortunately, choosing the optimal sequence of sub problems is dilcult due to the inability of the algorithm to consider the entire variable space at once. We propose a measure of iteration that we call momentum and derive a prediction method to minimize the momentum of the updated iterates hitting the boundary constraints. Our prediction method uses a rough heuristic set to choose an approximately optimal sub problem to solve. We show that this rough heuristic set could greatly improve the speed of the popular Sequential Minimal Optimization algorithm.

Item Type: Book Section
ISBN: 1098-7576
Additional Information:
Uncontrolled Keywords: ResPubID19079, convergence, iterative methods, minimisation, pattern classification, rough set theory, support vector machines
Subjects: FOR Classification > 1702 Cognitive Science
Faculty/School/Research Centre/Department > Centre for Ageing, Rehabilitation, Exercise & Sport Science (CARES)
Depositing User: VUIR
Date Deposited: 26 Apr 2013 00:47
Last Modified: 26 Apr 2013 00:47
URI: http://vuir.vu.edu.au/id/eprint/6162
DOI: https://doi.org/10.1109/IJCNN.2004.1380922
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Citations in Scopus: 0 - View on Scopus

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