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Evaluation of Particle Swarm Optimization for Support Vector Machine Training

Najaad OubeBlika Sofia Arkhstan Wang Zhang Lima Hongou
Volume 3

Abstract

It is simple to use particle swarm optimization to optimize a function with a set of linear
equalities. For
this specific purpose, the converging linear particle swarm optimization algorithm was devised.
Support vector machine training necessitates the resolution of a quadratic optimization
problem that is linearly constrained. Support vector machines are notoriously bad at handling
large datasets because they have a hard time dealing with the combinatorial proliferation that
happens when they try to solve many instances of this quadratic optimization problem. A
different way to train support vector machines that can handle large datasets has been suggested.
It uses the converging linear particle swarm optimization algorithm and a decomposition strategy.
This article lists a number of problems and issues that could happen when the converging linear
particle swarm optimization algorithm is used to train support vector machines. For example, a
problem with early convergence that happened because of the clamping method used in the first
algorithmic implementations is emphasized.
To address these issues, a proposed solution is provided.
Subsequently, an evaluation concerning the scalability of the suggested methodology is performe
d.

Keywords

Particle Swarm Optimisation,Support Vector Machines,Convex Optimisation

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