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Adaptive noise injection for input variables relevance determination

  • Part III: Learning: Theory and Algorithms
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Book cover 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

In this paper we consider the application of training with noise in multi-layer perceptron to input variables relevance determination. Noise injection is modified in order to penalize irrelevant features. The proposed algorithm is attractive as it requires the tuning of a single parameter. This parameter controls the penalization of the inputs together with the complexity of the model. After the presentation of the method, experimental evidences are given on simulated data sets.

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References

  1. L. Breiman, J.H. Friedman, R. Olshen, and C.J. Stone. Classification and Regression Trees. Wadswworth, Belmont, CA., 1984.

    Google Scholar 

  2. B. Efron and R.J. Tibshirani.An Introduction to the Bootstrap, volume 57 of Monographs on Statistics and Applied Probability. Chapman & Hall, New York, 1993.

    Google Scholar 

  3. J.H. Friedman. Flexible metric nearest neighbor classification. Technical report, Stanford University, Stanford, CA., November 1994.

    Google Scholar 

  4. Y. Grandvalet, S. Cann, and S. Boucheron. Noise injection: theoretical prospects. Neural Computation, 9(7), 1997, to appear.

    Google Scholar 

  5. T.J. Hastie and R.J. Tibshirani. Generalized Additive Models, volume 43 of Monographs on Statistics and Applied Probability. Chapman & Hall, New York, 1990.

    Google Scholar 

  6. L. Holmström and P. Koistinen. Using additive noise in back-propagation training. IEEE Transactions on Neural Networks, 3(1):24–38, January 1992.

    Google Scholar 

  7. T.K. Leen. From data distribution to regularization in invariant learning. Neural Computation, 7(5):974–981, 1995.

    Google Scholar 

  8. K. Matsuoka. Noise injection into inputs in back-propagation learning. IEEE Transactions on Systems, Man, and Cybernetics, 22(3):436–440, 1992.

    Google Scholar 

  9. R. M. Neal. Bayesian Learning for Neural Networks. Lecture Notes in Statistics. Springer-Verlag, New York, 1996.

    Google Scholar 

  10. A.R. Webb. Functional approximation by feed-forward networks: A least-squares approach to generalization. IEEE Transactions on Neural Networks, 5(3):363–371, 1994.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Heudiasyc, U.M.R. C.N.R.S. 6599, Université de Technologie de Compiègne, B.P. 20.529, 60205, Compiègne Cedex, France

    Yves Grandvalet & Stéphane Canu

Authors
  1. Yves Grandvalet
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  2. Stéphane Canu
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Editor information

Wulfram Gerstner Alain Germond Martin Hasler Jean-Daniel Nicoud

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© 1997 Springer-Verlag Berlin Heidelberg

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Grandvalet, Y., Canu, S. (1997). Adaptive noise injection for input variables relevance determination. 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/BFb0020198

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  • DOI: https://doi.org/10.1007/BFb0020198

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63631-1

  • Online ISBN: 978-3-540-69620-9

  • eBook Packages: Springer Book Archive

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