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Polynomial classifiers and support vector machines

  • Part III: Learning: Theory and Algorithms
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Artificial Neural Networks — ICANN'97 (ICANN 1997)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1327))

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Abstract

Polynomial support vector machines have shown a competitive performance for the problem of handwritten digit recognition. However, there is a large gap in performance vs. computing resources between the linear and the quadratic approach. By computing the complete quadratic classifier out of the quadratic support vector machine, a pivot point is found to trade between performance and effort. Different selection strategies are presented to reduce the complete quadratic classifier, which lower the required computing and memory resources by a factor of more than ten without affecting the generalization performance.

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References

  1. V. Vapnik: The Nature of Statistical Learning Theory; Springer-Verlag, New York, 1995.

    Google Scholar 

  2. J. Schürmann: Pattern Classification: A Unified View of Statistical and Neural Approaches; John Wiley & Sons, New York, 1996.

    Google Scholar 

  3. R. Wilkinson et al.: The First Census Optical Character Recognition Systems Conference; National Institute of Standards and Technology (NIST), Gaithersburg, 1992.

    Google Scholar 

  4. U. Kreßel: The Impact of' the Learning-Set Size in Handwritten-Digit Recognition; p. 1685–1689 in T. Kohonen et al. (eds.): Artificial Neural Networks; Proceedings of the International Conference on Artificial Neural Networks (ICANN91), North-Holland, Amsterdam, 1991.

    Google Scholar 

  5. C. Cortes and V. Vapnik: Support-Vector Networks; Machine Learning, Nr. 20, p. 273–297, 1995.

    Google Scholar 

  6. R. Courant and D. Hilbert: Methods of Mathematical Physics; J. Wiley, New York, 1953.

    Google Scholar 

  7. I. Graf.Adaption von Klassifikatoren mit Support-Vektoren; Master Thesis, Universität Ulm, Abteilung Informationstechnik, Ulm, 1997.

    Google Scholar 

  8. C. Burges: Simplified Support Vector Decision Rules; Proceedings of the 13th International Conference on Machine Learning, p. 71–77, Bari, 1996.

    Google Scholar 

  9. U. Kreßel and J. Schürmann: Pattern Classification Techniques Based on Function Approximation; chapter 2 in [11].

    Google Scholar 

  10. J. Franke: Isolated Handprinted Digit Recognition; chapter 4 in [11].

    Google Scholar 

  11. P. Wang and H. Bunke (eds.): Handbook on Optical Character Recognition and Document Image Analysis; World Scientific Publishing Company, 1997.

    Google Scholar 

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Wulfram Gerstner Alain Germond Martin Hasler Jean-Daniel Nicoud

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© 1997 Springer-Verlag Berlin Heidelberg

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Graf, I., Kreßel, U., Franke, J. (1997). Polynomial classifiers and support vector machines. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, JD. (eds) Artificial Neural Networks — ICANN'97. ICANN 1997. Lecture Notes in Computer Science, vol 1327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020187

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  • DOI: https://doi.org/10.1007/BFb0020187

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63631-1

  • Online ISBN: 978-3-540-69620-9

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