Analysis of Algebraic Expressions Derived from Genetic Multivariate Polynomials and Support Vector Machines: A Case Study
We discuss how algebraic explicit expressions modeling a complex phenomenon via an adequate set of data can be derived from the application of Genetic Multivariate Polynomials (GMPs), on the one hand, and Support Vector Machines (SVMs) on the other. A polynomial expression is derived in GMPs in a natural way, whereas in SVMs a polynomial kernel is employed to derive a similar one. In any particular problem an evolutionary determined sample of monomials is required in GMP expressions while, on the other hand, there is a large number of monomials implicit in the SVM approach. We make some experiments to compare the modeling characterization and accuracy obtained from the application of both methods.
KeywordsGenetic Algorithm Support Vector Machine Support Vector Regression Algebraic Expression Polynomial Kernel
Unable to display preview. Download preview PDF.
- 2.Kuri, A.: Approximation and Classification with Genetic Multivariate Polynomials. WSEAS Transactions on Computers (3), 645–652 (2006)Google Scholar
- 4.Kuri, A.: A Methodology for the Statistical Characterization of Genetic Algorithms. In: Proceedings of the Mexican International Congress on Artificial Intelligence, pp. 79–88. Springer-, Heidelberg (2002)Google Scholar
- 8.Smola, A., Schölkopf, B.: A Tutorial on Support Vector Regression. NeuroCOLT Technical Report NC-TR-98-030, Royal Holloway College, University of London, UK (1998)Google Scholar
- 9.Kuri, A., Mejía, I.: Evolutionary Training of SVM for Classification Problems with Self-Adaptive Parameters. In: Gelbukh, A., Monroy, R. (eds.) Advances in Artificial Intelligence Theory. IPN, pp. 207–216 (2005)Google Scholar
- 12.Chang, C., Lin, C.: LIBSVM: a library for support vector machines, Software (2001) available at :http://www.csie.ntu.edu.tw/~cjlin/libsvm/