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International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems

IEA/AIE 2001: Engineering of Intelligent Systems pp 326–332Cite as

Comparisons of QP and LP Based Learning from Empirical Data

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2070))

Abstract

The quadratic programming (QP) and the linear programming (LP) based method are recently the most popular learning methods from empirical data. Support vector machines (SVMs) are the newest models based on QP al- gorithm in solving the nonlinear regression and classification problems. The LP based learning also controls both the number of basis functions in a neural net- work (i.e., support vector machine) and the accuracy of learning machine. Both methods result in a parsimonious network. This results in data compression. Two different methods are compared in terms of number of SVs (possible compression achieved) and in generalization capability (i.e., error on unseen data).

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References

  1. Arthanari, T. S., Y. Dodge, 1993. Mathematical Programming in Statistics, J. Wiley & Sons., New York, NY

    MATH  Google Scholar 

  2. Bennett, K., 1999. Combining support vector and mathematical programming methods for induction. In B. Schölkopf, C. Burges, and A. Smola, Editors, Advances in Kernel Methods-SV Learning, pp 307–326, MIT Press, Cambridge, MA Charnes, A., W. W. Cooper, R. O. Ferguson, 1955. Optimal Estimation of Executive Compensation by Linear Programming, Manage. Sci., 1, 138

    Google Scholar 

  3. Cheney, E. W., A. A. Goldstein, 1958. Note on a paper by Zuhovickii concerning the Chebyshev problem for linear equations. J. Soc. Indust. Appl. Math., Volume 6, pp. 233–239

    Article  MATH  MathSciNet  Google Scholar 

  4. Eisenhart, C., 1962. Roger Joseph Boscovich and the Combination of Observationes, Actes International Symposium on R. J. Boskovic, pp. 19–25, Belgrade-Zagreb-Ljubljana, YU

    Google Scholar 

  5. Graepel, T., R. Herbrich, B. Schölkopf, A. Smola, P. Bartlett, K.-R. Müller, K. Obermayer, R. Williamson, 1999. Classification on proximity data with LP-machines, Proc. of the 9th Intl. Conf. on Artificial NN, ICANN 99, Edinburgh, 7-10 Sept.

    Google Scholar 

  6. Hadzic, I., 1999. Learning from Experimental Data by Linear Programming Selected Support Vectors, PhD Thesis, (work in progress), The University of Auckland, Auckland, NZ

    Google Scholar 

  7. Hadzic, I., V. Kecman, 1999. Learning from Data by Linear Programming, NZ Postgraduate Conference Proceedings, Auckland, Dec. 15-16

    Google Scholar 

  8. Kecman, V., 2001. Learning and Soft Computing, SVM, NN, and FLS, The MIT Press, Cambridge, MA

    Google Scholar 

  9. Kecman V., Hadzic I., 2000. Support Vectors Selection by Linear Programming, Proceedings of the International Joint Conference on Neural Networks (IJCNN 2000), Vol. 5, pp. 193–199, Como, Italy

    Google Scholar 

  10. Kelley E. J. jr., 1958. An application of linear programming to curve fitting (received by editors October 11, (1957), J. Soc. Indust. Appl. Math., Volume 6, pp. 15–22

    Article  MATH  MathSciNet  Google Scholar 

  11. Mangasarian, O. L., 1965. Linear and Nonlinear Separation of Patterns by Linear Programming, Operations Research 13, pp. 444–452.

    Google Scholar 

  12. Poggio, T., F. Girosi, 1993. Learning, Approximation and Networks, Lectures and Lectures’ Handouts-Course 9.520, MIT, Cambridge, MA.

    Google Scholar 

  13. Rice R. J., 1964. The Approximation of Functions, Addison-Wesley Publishing Company, Reading, Massachusetts, Palo Alto, London

    MATH  Google Scholar 

  14. Smola A., T. T. Friess, B. Schölkopf, 1998. Semiparametric Support Vector and Linear Programming Machines, NeuroCOLT2 Technical Report Series, NC2-TR-1998-024

    Google Scholar 

  15. Smola, A., B. Schölkopf, G. Rätsch, 1999. Linear Programs for Automatic Accuracy Control in Regression, submitted to ICANN’99

    Google Scholar 

  16. Stiefel, E., 1960. Note on Jordan Elimination, Linear Programming and Chebyshev Approximation, Numer. Math., 2, 1.

    Google Scholar 

  17. Vapnik, V. N., 1995. The Nature of Statistical Learning Theory, Springer Verlag Inc, New York, NY

    MATH  Google Scholar 

  18. Weston, J., A. Gammerman, M. O. Stitson, V. Vapnik, V. Vovk, C. Watkins, 1999. Support Vector Density Estimation, In B. Schölkopf, C. Burges, and A. Smola, Editors, Advances in Kernel Methods-SV Learning, p.p. 307–326, MIT Press, Cambridge, MA

    Google Scholar 

  19. Zhang, Q. H., J.-J. Fuchs, 1999. Building neural networks through linear programming, Proceedings of 14th IFAC Triennial World Congress, Vol. K, pp. 127–132, Pergamon Press

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, University of Auckland, Private Bag, 92019, Auckland, New Zealand

    Vojislav Kecman & Tiru Arthanari

Authors
  1. Vojislav Kecman
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  2. Tiru Arthanari
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Editor information

Editors and Affiliations

  1. Hungarian Academy of Sciences, Intelligent Manufacturing and Business Processes Computer and Automation Research Institute, Kende utca 13-17, 1111, Budapest, Hungary

    László Monostori  & József Váncza  & 

  2. Department of Computer Science 601 University Drive, Southwest Texas State University, San Marcos, TX, 78666-4616, USA

    Moonis Ali

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© 2001 Springer-Verlag Berlin Heidelberg

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Kecman, V., Arthanari, T. (2001). Comparisons of QP and LP Based Learning from Empirical Data. In: Monostori, L., Váncza, J., Ali, M. (eds) Engineering of Intelligent Systems. IEA/AIE 2001. Lecture Notes in Computer Science(), vol 2070. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45517-5_36

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  • DOI: https://doi.org/10.1007/3-540-45517-5_36

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42219-8

  • Online ISBN: 978-3-540-45517-2

  • eBook Packages: Springer Book Archive

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