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Machine Learning Challenges Workshop

MLCW 2005: Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment pp 33–40Cite as

A Pragmatic Bayesian Approach to Predictive Uncertainty

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 3944)

Abstract

We describe an approach to regression based on building a probabilistic model with the aid of visualization. The “stereopsis” data set in the predictive uncertainty challenge is used as a case study, for which we constructed a mixture of neural network experts model. We describe both the ideal Bayesian approach and computational shortcuts required to obtain timely results.

Keywords

  • Markov Chain Monte Carlo
  • Loss Function
  • Input Space
  • Predictive Distribution
  • Markov Chain Monte Carlo Method

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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  • DOI: 10.1007/11736790_3
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References

  1. Sinz, F.H., Candela, J.Q., Bakır, G.H., Rasmussen, C.E., Franz, M.O.: Learning depth from stereo. In: Rasmussen, C.E., Bülthoff, H.H., Schölkopf, B., Giese, M.A. (eds.) DAGM 2004. LNCS, vol. 3175, pp. 245–252. Springer, Heidelberg (2004)

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Author information

Authors and Affiliations

  1. Gatsby Computational Neuroscience Unit, University College London, London, WC1N 3AR, UK

    Iain Murray & Edward Snelson

Authors
  1. Iain Murray
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  2. Edward Snelson
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Editor information

Editors and Affiliations

  1. Max Planck Institute for Biological Cybernetics, Spemannstr. 38, Tübingen, Germany

    Joaquin Quiñonero-Candela

  2. Bar Ilan University, 52900, Ramat Gan, Israel

    Ido Dagan

  3. ITC-IRST, Trento, Italy

    Bernardo Magnini

  4. Université d’Evry-Val d’Essonne, IBISC CNRS FRE 2873 and GENPOLE, 523, Place des terrasses, 91000, Evry, France

    Florence d’Alché-Buc

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© 2006 Springer-Verlag Berlin Heidelberg

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Murray, I., Snelson, E. (2006). A Pragmatic Bayesian Approach to Predictive Uncertainty. In: Quiñonero-Candela, J., Dagan, I., Magnini, B., d’Alché-Buc, F. (eds) Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment. MLCW 2005. Lecture Notes in Computer Science(), vol 3944. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11736790_3

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  • DOI: https://doi.org/10.1007/11736790_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33427-9

  • Online ISBN: 978-3-540-33428-6

  • eBook Packages: Computer ScienceComputer Science (R0)