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comp.ai.neural-nets FAQ, Part 3 of 7: Generalization

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See reader questions & answers on this topic! - Help others by sharing your knowledge
Copyright 1997, 1998, 1999, 2000, 2001, 2002 by Warren S. Sarle, Cary, NC,
USA. Answers provided by other authors as cited below are copyrighted by
those authors, who by submitting the answers for the FAQ give permission for
the answer to be reproduced as part of the FAQ in any of the ways specified
in part 1 of the FAQ. 

This is part 3 (of 7) of a monthly posting to the Usenet newsgroup
comp.ai.neural-nets. See the part 1 of this posting for full information
what it is all about.

========== Questions ========== 
********************************

Part 1: Introduction
Part 2: Learning
Part 3: Generalization

   How is generalization possible?
   How does noise affect generalization?
   What is overfitting and how can I avoid it?
   What is jitter? (Training with noise)
   What is early stopping?
   What is weight decay?
   What is Bayesian learning?
   How to combine networks?
   How many hidden layers should I use?
   How many hidden units should I use?
   How can generalization error be estimated?
   What are cross-validation and bootstrapping?
   How to compute prediction and confidence intervals (error bars)?

Part 4: Books, data, etc.
Part 5: Free software
Part 6: Commercial software
Part 7: Hardware and miscellaneous

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Send corrections/additions to the FAQ Maintainer:
saswss@unx.sas.com (Warren Sarle)





Last Update March 27 2014 @ 02:11 PM