Training and analysis of support vector machine using sequential minimal optimization

S. Shahbudin, Aini Hussain, Salina Abdul Samad, N. Md. Tahir

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Maximizing the classification performance of the training data is a typical procedure in training a classifier. It is well known that training a Support Vector Machine (SVM) requires the solution of an enormous quadratic programming (QP) optimization problem. Serious challenges appeared in the training dilemma due to immense training and this could be solved using Sequential Minimal Optimization (SMO). This paper investigates the performance of SMO solver in term of CPU time, number of support vector and decision boundaries when applied in a 2-dimensional datasets. Next, the chunking algorithm is employed for comparison purpose. Initial results demonstrated that the SMO algorithm could enhance the performance of the training dataset. Both algorithms illustrated similar patterns from the decision boundaries attained. Classification rate achieved by both solvers are superb.

Original languageEnglish
Title of host publicationConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Pages373-378
Number of pages6
DOIs
Publication statusPublished - 2008
Event2008 IEEE International Conference on Systems, Man and Cybernetics, SMC 2008 - Singapore, Singapore
Duration: 12 Oct 200815 Oct 2008

Other

Other2008 IEEE International Conference on Systems, Man and Cybernetics, SMC 2008
CountrySingapore
CitySingapore
Period12/10/0815/10/08

Fingerprint

Support vector machines
Quadratic programming
Program processors
Classifiers

Keywords

  • Chunking algorithm
  • Decision boundaries
  • Sequential Minimal Optimization
  • Support vector machine

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Human-Computer Interaction

Cite this

Shahbudin, S., Hussain, A., Abdul Samad, S., & Md. Tahir, N. (2008). Training and analysis of support vector machine using sequential minimal optimization. In Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics (pp. 373-378). [4811304] https://doi.org/10.1109/ICSMC.2008.4811304

Training and analysis of support vector machine using sequential minimal optimization. / Shahbudin, S.; Hussain, Aini; Abdul Samad, Salina; Md. Tahir, N.

Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics. 2008. p. 373-378 4811304.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Shahbudin, S, Hussain, A, Abdul Samad, S & Md. Tahir, N 2008, Training and analysis of support vector machine using sequential minimal optimization. in Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics., 4811304, pp. 373-378, 2008 IEEE International Conference on Systems, Man and Cybernetics, SMC 2008, Singapore, Singapore, 12/10/08. https://doi.org/10.1109/ICSMC.2008.4811304
Shahbudin S, Hussain A, Abdul Samad S, Md. Tahir N. Training and analysis of support vector machine using sequential minimal optimization. In Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics. 2008. p. 373-378. 4811304 https://doi.org/10.1109/ICSMC.2008.4811304
Shahbudin, S. ; Hussain, Aini ; Abdul Samad, Salina ; Md. Tahir, N. / Training and analysis of support vector machine using sequential minimal optimization. Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics. 2008. pp. 373-378
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