Title page for ETD etd-08062007-130341

Document Type

Master's Dissertation

Author

Paquet, Ulrich

URN

etd-08062007-130341

Document Title

Training support vector machines with particle swarms

Degree

MSc (Computer Science)

Department

Computer Science

Supervisor

Advisor Name	Title
Prof A P Engelbrecht

Keywords

Mathematical organization computer programs
Computer algoriths
Machine learning
Stochastic processes
Artificial Intelligence computer programs

Date

2004-04-27

Availability

unrestricted

Abstract

Particle swarms can easily be used to optimize a function with a set of linear equality constraints, by restricting the swarm’s movement to the constrained search space. A “Linear Particle Swarm Optimiser” and “Converging Linear Particle Swarm Optimiser” is developed to optimize linear equality-constrained functions. It is shown that if the entire swarm of particles is initialized to consist of only feasible solutions, then the swarm can optimize the constrained objective function without ever again considering the set of constraints. The Converging Linear Particle Swarm Optimiser overcomes the Linear Particle Swarm Optimiser’s possibility of premature convergence. Training a Support Vector Machine requires solving a constrained quadratic programming problem, and the Converging Linear Particle Swarm Optimiser ideally fits the needs of an optimization method for Support Vector Machine training. Particle swarms are intuitive and easy to implement, and is presented as an alternative to current numeric Support Vector Machine training methods.

Files

Filename		Size	Approximate Download Time (Hours:Minutes:Seconds)
Filename		Size	28.8 Modem	56K Modem	ISDN (64 Kb)	ISDN (128 Kb)	Higher-speed Access
	00front.pdf	1.56 Mb	00:07:13	00:03:42	00:03:15	00:01:37	00:00:08
	01chapter1.pdf	2.37 Mb	00:10:56	00:05:37	00:04:55	00:02:27	00:00:12
	02chapter2.pdf	2.81 Mb	00:13:00	00:06:41	00:05:51	00:02:55	00:00:14
	03chapter3.pdf	1.50 Mb	00:06:55	00:03:33	00:03:07	00:01:33	00:00:07
	04chapter4.pdf	4.17 Mb	00:19:19	00:09:56	00:08:41	00:04:20	00:00:22
	05chapter5.pdf	3.70 Mb	00:17:06	00:08:48	00:07:42	00:03:51	00:00:19
	06chapter6.pdf	425.38 Kb	00:01:58	00:01:00	00:00:53	00:00:26	00:00:02
	07bibliography.pdf	1.04 Mb	00:04:48	00:02:28	00:02:09	00:01:04	00:00:05