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
Advisor Name Title
Prof A P Engelbrecht
  • Mathematical organization computer programs
  • Computer algoriths
  • Machine learning
  • Stochastic processes
  • Artificial Intelligence computer programs
Date 2004-04-27
Availability unrestricted
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.

  Filename       Size       Approximate Download Time (Hours:Minutes:Seconds) 
 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

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