Abstract
Support Vector Machines (SVMs) are learning machines that can perform binary classification (pattern recognition) and real valued function approximation (regression estimation) tasks. An inverse problem of SVMs is how to split a given dataset into two clusters such that the maximum margin between the two clusters is attained. Here the margin is defined according to the separating hyper-plane generated by support vectors. This paper investigates the inverse problem of SVMs by designing a parallel genetic algorithm. Experiments show that this algorithm can greatly decrease time complexity by the use of parallel processing. This study on the inverse problem of SVMs is motivated by designing a heuristic algorithm for generating decision trees with high generalization capability.
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References
Vapnik, V.N.: Statistical learning theory. Wiley, New York (1998) ISBN: 0-471-03003-1
Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (2000) ISBN: 0-387-98780-0
Vapnik, V.N.: An Overview of Statistical Learning Theory. IEEE Transactions on Neural Networks 10(5), 988–999 (1999)
Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Guyon, D.H. (ed.) Proc. Fifth Annual Workshop on Computational Learning Theory, pp. 144–152. ACM Press, Pittsburgh (1992)
Schapire, R., Freund, Y., Bartlett, P., Sun Lee, W.: Boosting the margin: A new explanation for the effectiveness of voting methods. Ann. Statist. 26(5), 1651–1686 (1998)
Shawe-Taylor, J., Bartlett, P.L., Williamson, R.C., Anthony, M.: Structural risk minimization over data-dependent hierarchies. IEEE Trans. Inform. Theory 44, 1926–1940 (1998)
Lin, C.-T., Lee, C.S.G.: Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems, 797 p. Prentice Hall PTR, Englewood Cliffs (1996) ISBN 0-13-235169-2
Cantú-Paz, E.: A survey of parallel genetic algorithms. Tech. Rep., The University of Illinois, IlliGAL Report No. 97003 (1997), FTP address ftp://ftpilligal.ge.uiuc.edu/pub/papers/IlliGALs/97003.ps.Z
UCI Repository of machine learning databases and domain theories. FTP address, ftp://ftp.ics.uci.edu/pub/machine-learning-databases
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© 2006 Springer-Verlag Berlin Heidelberg
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He, Q., Wang, X., Chen, J., Yan, L. (2006). A Parallel Genetic Algorithm for Solving the Inverse Problem of Support Vector Machines. In: Yeung, D.S., Liu, ZQ., Wang, XZ., Yan, H. (eds) Advances in Machine Learning and Cybernetics. Lecture Notes in Computer Science(), vol 3930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11739685_91
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DOI: https://doi.org/10.1007/11739685_91
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33584-9
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