Abstract
This paper proposes a new combined kernel function and its learning method for support vector machine which results in higher learning rate and better classification performance. A set of simple kernel functions are combined to create a new kernel function, which is trained by a learning method employing evolutionary algorithm. The learning method results in the optimal decision model consisting of a set of features as well as a set of the parameters for combined kernel function. The new kernel function and the learning method were applied to obtain the optimal decision model for classification of proteome patterns, and in the comparison with other kernel functions, the combined kernel function showed a higher convergence rate and a greater flexibility in learning a problem space than single kernel functions.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Cristianini, N., Shawe-Taylor, J.: An introduction to Support Vector Machines and other kernel-based learning methods, Cambridge (2000)
Vapnik, V.N., et al.: Theory of Support Vector Machines, Technical Report CSD TR-96-17. Univ. of London (1996)
Kecman, V.: Learning and Soft Computing: Support Vector Machines. In: Neural Networks, and Fuzzy Logic Models (Complex Adaptive Systems). The MIT press, Cambridge (2001)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. John Wiley & Sons Inc., Chichester (2001)
Joachims, T.: Making large-Scale SVM Learning Practical. In: Advances in Kernel Methods - Support Vector Learning, ch. 11. MIT Press, Cambridge (1999)
Schökopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2002)
Michalewicz, Z.: Genetic Algorithms + Data structures = Evolution Programs, 3rd rev. and extended edn. Springer, Heidelberg (1996)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization & Machine Learning. Addison-Wesley, Reading (1989)
Mitchell, M.: Introduction to genetic Algorithms. Fifth printing. MIT Press, Cambridge (1999)
Rüping, S.: mySVM-Manual. University of Dortmund, Lehrstuhl Informatik (2000), http://www-ai.cs.uni-dortmund.de/SOFTWARE/MYSVM/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nguyen, HN., Ohn, SY., Choi, WJ. (2004). Combined Kernel Function for Support Vector Machine and Learning Method Based on Evolutionary Algorithm. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds) Neural Information Processing. ICONIP 2004. Lecture Notes in Computer Science, vol 3316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30499-9_198
Download citation
DOI: https://doi.org/10.1007/978-3-540-30499-9_198
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23931-4
Online ISBN: 978-3-540-30499-9
eBook Packages: Springer Book Archive