NDLI: A Modified Particle Swarm Optimization Algorithm for Support Vector Machine Training

Content Provider	IEEE Xplore Digital Library
Author	Hejin Yuan Yanning Zhang Dengfu Zhang Gen Yang
Copyright Year	2006
Description	Author affiliation: Sch. of Comput. Sci., Northwestern Polytech. Univ., Xi'an (Hejin Yuan; Yanning Zhang; Dengfu Zhang; Gen Yang)
Abstract	A new modified particle swarm optimization algorithm for linear equation constrained optimization problem was put forward. And the method using this algorithm to train support vector machine was given. In the new algorithm, the particle studies not only from itself and the best one but also from other particles in the population with certain probability. This strengthened learning behavior can make the particle to search the whole solution space better. In addition, the mutation for the particle is considered in the new algorithm. The mutation operation is executed when the particle swarm becomes stagnated, which is decided by calculating the population diversity with the formula presented in this paper. For the specific constraints of support vector machine, a new method to initialize the particles in the feasible solution space was provided. The experiments on synthetic and sonar dataset classification show that our algorithm is feasible and robust for support vector machine training
Starting Page	4128
Ending Page	4132
File Size	482306
Page Count	5
File Format	PDF
ISBN	1424403324
DOI	10.1109/WCICA.2006.1713151
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Particle swarm optimization Support vector machines Support vector machine classification Genetic mutations Equations Kernel Optimization methods Computer science Constraint optimization Sonar mutation support vector machine particle swarm optimization algorithm
Content Type	Text
Resource Type	Article

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