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comp.ai.neural-nets FAQ, Part 3 of 7: Generalization
Archive-name: ai-faq/neural-nets/part3 Last-modified: 2001-05-21 URL: ftp://ftp.sas.com/pub/neural/FAQ3.html Maintainer: saswss@unx.sas.com (Warren S. Sarle) 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
Section Contents
- News Headers
- 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