A dynamic system framework for the decomposition method solving Support Vector Machines

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Lai, Daniel ORCID: 0000-0003-3459-7709, Mani, N and Palaniswami, M (2004) A dynamic system framework for the decomposition method solving Support Vector Machines. In: ‬Proceedings of the 2004 Intelligent Sensors, Sensor Networks & Information Processing Conference :‎ ‬14-17 December 2004, Melbourne, Australia. IEEE, Adelaide, South Australia, pp. 283-288.


The decomposition method is generally used to solve the quadratic program of Support Vector Machines. The rate of convergence of this method is largely dependant on the sequence of sub-problems solved. In order to study ways of increasing the convergence, we propose a dynamic system perspective to model the dynamics of the decomposition method. In particular, the minimization of a sub-problem can be viewed as an autonomous dissipative system in terms of second order differential equations. The gradients of the sub-problems and the inequality constraints are explicitly modelled as system variables. Using these models, we then define a general decomposition method as a non-autonomous system composed of sub-systems that operate for discrete time intervals. The dependance of this system on time is depicted by a time dependant permutation matrix which functions as an indicator for operating subsystem components.

Additional Information

‬IEEE cat. no. 04EX994C
‬Alternative titles :
ISSNIP 2004‬
Intelligent Sensors, Sensor Networks and Information Processing Conference

Item type Book Section
URI https://vuir.vu.edu.au/id/eprint/5558
Official URL https://ieeexplore.ieee.org/document/1417476
ISBN 0780388941
Subjects Historical > FOR Classification > 0102 Applied Mathematics
Historical > FOR Classification > 0802 Computation Theory and Mathematics
Historical > FOR Classification > 0902 Automotive Engineering
Historical > Faculty/School/Research Centre/Department > Centre for Ageing, Rehabilitation, Exercise & Sport Science (CARES)
Keywords ResPubID19078, support vector machines, SVM, nonlinear function, decomposition method, computing memory, optimization, chunking, algorithm, working set, search direction, a linear convergence rate, convergence
Citations in Scopus 0 - View on Scopus
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