Open Access
EPJ Web of Conferences
Volume 55, 2013
SOS 2012 – IN2P3 School of Statistics
Article Number 02003
Number of page(s) 18
Section Multivariate Analysis Tools
Published online 01 July 2013
  1. D.J.C. MacKay, Information Theory, Inference, and Learning Algorithms, Cambridge University Press, 2003,
  2. D.J.C. MacKay, “Bayesian Methods for Neural Networks: Theory and Application, Course notes for Neural Networks Summer School, 1995,
  3. T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning, Springer, 2nd edition, 2009 [CrossRef] [MathSciNet]
  4. C.M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006
  5. Y. LeCun, L. Bottou, G.B. Orr, K.R. Mueller, “Efficient BackProp”, in Neural Networks: Tricks of the trade, Springer, 1998
  6. D.E. Rumelhart, G.E. Hinton, R.J. Williams, “Learning representations by back-propagating errors”, in Nature, 323 533–536 [NASA ADS] [CrossRef]
  7. A. Hoecker, P. Speckmayer, J. Stelzer, J. Therhaag, E. von Toerne, and H. Voss, “TMVA: Toolkit for Multivariate Data Analysis”, PoS A CAT 040 (2007) [physics/0703039].
  8. M. Feindt, U. Kerzel, “The NeuroBayes neural network package”, Nuclear Instruments and Methods in Physics Research, 2006, Vol. 559 Issue 1, 190–194 [CrossRef]
  9. R.M. Neal, “Priors for infinite networks”, Technical Report CRG-TR-94-1, Dept. of Computer Science, University of Toronto, 1994