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A boosting algorithm for regression

  • A. Bertoni
  • P. Campadelli
  • M. Parodi
Part I: Coding and Learning in Biology
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1327)

Abstract

A new boosting algorithm ADABOOST-Ra for regression problems is presented and upper bound on the error is obtained. Experimental results to compare ADABOOST-RΔ and other learning algorithms are given.

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References

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    Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. Internal Report of AT & T, September (1995)Google Scholar
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    Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. Machine Learning: Proc. of Thirteenth Int. Conf. (1996) 148–156Google Scholar
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    V.N. Vapnik (1982) Estimation of Dependences Based on Empirical Data. Springer-Verlag.Google Scholar
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    V.N. Vapnik, A.Y. Chervonenkis (1971) On the uniform convergence of relative frequencies of events to their probabilities. Theory of Probability and its Applications, 16(2): 264–280.Google Scholar
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    Freund, Y.: Boosting a weak learning algorithm by majority. Information and Computation 121 (2) (1995) 256–285Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • A. Bertoni
    • 1
  • P. Campadelli
    • 1
  • M. Parodi
    • 1
  1. 1.Dipartimento di Scienze dell'InformazioneUniversità degli Studi di MilanoMilanoItaly

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