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A Novel Simulated Annealing-Based Learning Algorithm for Training Support Vector Machines

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Intelligent Systems Design and Applications (ISDA 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 557))

Abstract

A theoretical advantage of large margin classifiers such as support vector machines (SVM) concerns the empirical and structural risk minimization which balances the complexity of the model against its success at fitting the training data. Metaheuristics have been used to work with SVMs in order to select features, tune hypeparameters or even achieve a reduced-set of support vectors. In spite of such tasks being interesting, metaheuristics such as simulated annealing (SA) do not play an important role in the process of solving the quadratic optimization problem, which arises from support vector machines. To do so, well-known methods such as sequential minimal optimization, kernel adatron or even classical mathematical methods have been used with this goal. In this paper, we propose to use simulated annealing in order to solve such a quadratic optimization problem. Our proposal is interesting when compared with those aforementioned methods, since it is simple and achieved similar (or even higher) accuracy and high sparseness in the solution.

The authors would like to thank the IFCE and CAPES for supporting their research.

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References

  1. Anlauf, J.K., Biehl, M.: The adatron: an adaptive perceptron algorithm. Europhys. Lett. 10(7), 687 (1989)

    Article  Google Scholar 

  2. Blake, C., Merz, C.J.: UCI repository of machine learning databases (1998)

    Google Scholar 

  3. Dumitrescu, D., Preuss, M., Stoean, C., Stoean, R.: Evolutionary support vector machines and their application for classification (2006)

    Google Scholar 

  4. Černý, V.: Thermodynamical approach to the traveling salesman problem: an efficient simulation algorithm. J. Optim. Theor. Appl. 45(1), 41–51 (1985)

    Article  MathSciNet  MATH  Google Scholar 

  5. Gill, P.E., Murray, W., Wright, M.H.: Practical Optimization. Academic Press, London (1981)

    MATH  Google Scholar 

  6. Hollander, M., Wolfe, D.A., Chicken, E.: Nonparametric Statistical Methods. Wiley, New York (2013)

    MATH  Google Scholar 

  7. Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  8. Li, Y., Tong, Y., Bai, B., Zhang, Y.: An improved particle swarm optimization for svm training. In: Third International Conference on Natural Computation (ICNC 2007), vol. 2, pp. 611–615. IEEE (2007)

    Google Scholar 

  9. Michalewicz, Z., Janikow, C.Z.: Handling constraints in genetic algorithms. In: ICGA, pp. 151–157 (1991)

    Google Scholar 

  10. Mierswa, I.: Evolutionary learning with kernels: a generic solution for large margin problems. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, pp. 1553–1560. ACM (2006)

    Google Scholar 

  11. Paquet, U.: Training support vector machines with particle swarms (2007)

    Google Scholar 

  12. Paquet, U., Engelbrecht, A.P.: A new particle swarm optimiser for linearly constrained optimisation. In: The 2003 Congress on Evolutionary Computation, CEC 2003, vol. 1, pp. 227–233. IEEE (2003)

    Google Scholar 

  13. Platt, J.C.: Fast training of support vector machines using sequential minimal optimization. In: Advances in Kernel Methods - Support Vector Learning. MIT Press, Cambridge, MA, USA (1999)

    Google Scholar 

  14. Neto, A.R.R., Barreto, G.A.: Opposite maps: vector quantization algorithms for building reduced-set SVM and lSSVM classifiers. Neural Process. Lett. 37(1), 3–19 (2013)

    Article  Google Scholar 

  15. Silva, A., Gonçalves, T.: Training support vector machines with an heterogeneous particle swarm optimizer. In: Tomassini, M., Antonioni, A., Daolio, F., Buesser, P. (eds.) ICANNGA 2013. LNCS, vol. 7824, pp. 100–109. Springer, Heidelberg (2013). doi:10.1007/978-3-642-37213-1_11

    Chapter  Google Scholar 

  16. Silva, D.A., Silva, J.P., Neto, A.R.R.: Novel approaches using evolutionary computation for sparse lssvms. Neurocomput. 168, 908–916 (2015)

    Article  Google Scholar 

  17. Stoean, R., Dumitrescu, D., Preuss, M., Stoean, C.: Evolutionary support vector regression machines. In: Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2006, pp. 330–335 (2006)

    Google Scholar 

  18. Stoean, R., Preuss, M., Stoean, C., Dumitrescu, D.: Concerning the potential of evolutionary support vector machines. In: IEEE Congress on Evolutionary Computation, CEC 2007, pp. 1436–1443. IEEE (2007)

    Google Scholar 

  19. Vapnik, V.N.: Statistical Learning Theory. Wiley-Interscience, New York (1998)

    MATH  Google Scholar 

  20. Yuan, H., Zhang, Y., Zhang, D., Yang, G.: A modified particle swarm optimization algorithm for support vector machine training. In: 2006 6th World Congress on Intelligent Control and Automation, vol. 1, pp. 4128–4132. IEEE (2006)

    Google Scholar 

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Authors and Affiliations

  1. Department of Teleinformatics, Federal Institute of Ceará (IFCE), Av. Treze de Maio, 2081, Benfica CEP, Fortaleza, Ceará, 60040-215, Brazil

    Madson L. Dantas Dias & Ajalmar R. Rocha Neto

Authors
  1. Madson L. Dantas Dias
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  2. Ajalmar R. Rocha Neto
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Corresponding author

Correspondence to Ajalmar R. Rocha Neto .

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Editors and Affiliations

  1. Departamento de Engenharia Informática, Instituto Superior de Engenharia do Port, Porto, Portugal

    Ana Maria Madureira

  2. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs, Auburn, Washington, USA

    Ajith Abraham

  3. Polytechnic Institute of Porto, Felgueiras, Portugal

    Dorabela Gamboa

  4. Campus of Gualtar, University of Minho, Braga, Portugal

    Paulo Novais

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Dias, M.L.D., Neto, A.R.R. (2017). A Novel Simulated Annealing-Based Learning Algorithm for Training Support Vector Machines. In: Madureira, A., Abraham, A., Gamboa, D., Novais, P. (eds) Intelligent Systems Design and Applications. ISDA 2016. Advances in Intelligent Systems and Computing, vol 557. Springer, Cham. https://doi.org/10.1007/978-3-319-53480-0_34

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  • DOI: https://doi.org/10.1007/978-3-319-53480-0_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-53479-4

  • Online ISBN: 978-3-319-53480-0

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