Abstract
I submitted entries for the two classification problems — “Catalysis” and “Gatineau” — in the Evaluating Predictive Uncertainty Challenge. My entry for Catalysis was the best one; my entry for Gatineau was the third best, behind two similar entries by Nitesh Chawla.
The Catalysis dataset was later revealed to be about predicting a property of yeast proteins from expression levels of the genes encoding them. The nature of the Gatineau dataset has not been revealed, for proprietary reasons. The two datasets are similar in number of input variables that are available for predicting the binary outcome (617 for Catalysis, 1092 for Gatineau). They differ substantially in the number of cases available for training (1173 for Catalysis, 5176 for Gatineau) and in the fractions of cases that are in the two classes (43%/57% for Catalysis, 9%/91% for Gatineau).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Lampinen, J., Vehtari, A.: Bayesian approach for neural networks — review and case studies. Neural Networks 14, 257–274 (2001)
Liu, J.S.: Monte Carlo Strategies in Scientific Computing. Springer, Heidelberg (2001)
Neal, R.M.: Probabilistic Inference Using Markov Chain Monte Carlo Methods, Technical Report CRG-TR-93-1, Department of Computer Science, University of Toronto, 144 p. (1993) Available from, http://www.cs.utoronto.ca/~radford/
Neal, R.M.: Bayesian Learning for Neural Networks. Lecture Notes in Statistics, vol. 118. Springer, Heidelberg (1996)
Neal, R.M., Zhang, J.: High Dimensional Classification with Bayesian Neural Networks and Dirichlet Diffusion Trees. In: Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L. (eds.) Feature Extraction, Foundations and Applications. Studies in Fuzziness and Soft Computing. Physica-Verlag, Springer, Heidelberg (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Neal, R.M. (2006). Classification with Bayesian Neural Networks. 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_2
Download citation
DOI: https://doi.org/10.1007/11736790_2
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)