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Optimal linear regression on classifier outputs

  • Part III: Learning: Theory and Algorithms
  • Conference paper
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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

We consider the combination of the outputs of several classifiers trained independently for the same discrimination task. We introduce new results which provide optimal solutions in the case of linear combinations. We compare our solutions to existing ensemble methods and characterize situations where our approach should be preferred.

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References

  1. Bates, J.M. and Granger, C.W.J. (1969): The Combination of Forecasts. Operational Research Quaterly, Vol. 20, 451–468.

    Google Scholar 

  2. Bishop, C.M. (1995): Neural Networks for Pattern Recognition, Clarendon Press, Oxford.

    Google Scholar 

  3. Guermeur, Y., d'Alché-Buc, F. and Gallinari, P. (1997): Combinaison Linéaire Optimale de Classifieurs. XXIX-ièmes Journées de Statistique, 425–428.

    Google Scholar 

  4. Guermeur, Y. and Gallinari, P. (1996): Combining Statistical Models for Protein Secondary Structure Prediction. ICANN'96, Bochum, 599–604.

    Google Scholar 

  5. LeBlanc, M. and Tibshirani, R. (1993): Combining estimates in regression and classification. Technical Report 9318, Department of Preventive Medicine and Biostatistics and Department of Statistics, University of Toronto.

    Google Scholar 

  6. Rosen, J.B. (1960): The Gradient Projection Method for Nonlinear Programming. Part 1. Linear Constraints. J. Soc. Indust. Appl. Math., Vol. 8, N o 1, 181–217.

    Google Scholar 

  7. Perrone, M.P. and Cooper, L.N. (1993): When Networks Disagree: Ensemble Methods for Hybrid Neural Networks. Technical Report, Institute for Brain and Neural Systems, Brown University, Providence, Rhode Island.

    Google Scholar 

  8. Rost, B. and Sander, C. (1993): Prediction of Protein Secondary Structure at Better than 70% Accuracy. J. Mol. Biol., 232, 584–599.

    Google Scholar 

  9. Wolpert, D.H. (1992): Stacked Generalization. Neural Networks, Vol. 5, 241–259.

    Google Scholar 

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Author information

Authors and Affiliations

  1. LIP6, Université Pierre et Marie Curie, Tours 46-00, Boîte 169 4, Place Jussieu, 75252, Paris cedex 05, France

    Yann Guermeur, Florence d'Alché-Buc & Patrick Gallinari

Authors
  1. Yann Guermeur
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  2. Florence d'Alché-Buc
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  3. Patrick Gallinari
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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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Guermeur, Y., d'Alché-Buc, F., Gallinari, P. (1997). Optimal linear regression on classifier outputs. 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/BFb0020201

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  • DOI: https://doi.org/10.1007/BFb0020201

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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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