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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 2 (of 7) of a monthly posting to the Usenet newsgroup See the part 1 of this posting for full information
what it is all about.

========== Questions ========== 

Part 1: Introduction
Part 2: Learning

   What are combination, activation, error, and objective functions?
      Combination functions
      Activation functions
      Error functions
      Objective functions
   What are batch, incremental, on-line, off-line, deterministic,
   stochastic, adaptive, instantaneous, pattern, epoch, constructive, and
   sequential learning?
      Batch vs. Incremental Learning (also Instantaneous, Pattern, and
      On-line vs. Off-line Learning
      Deterministic, Stochastic, and Adaptive Learning
      Constructive Learning (Growing networks)
      Sequential Learning, Catastrophic Interference, and the
      Stability-Plasticity Dilemma
   What is backprop?
   What learning rate should be used for backprop?
   What are conjugate gradients, Levenberg-Marquardt, etc.?
   How does ill-conditioning affect NN training?
   How should categories be encoded?
   Why not code binary inputs as 0 and 1?
   Why use a bias/threshold?
   Why use activation functions?
   How to avoid overflow in the logistic function?
   What is a softmax activation function?
   What is the curse of dimensionality?
   How do MLPs compare with RBFs?
      Hybrid training and the curse of dimensionality
      Additive inputs
      Redundant inputs
      Irrelevant inputs
   What are OLS and subset/stepwise regression?
   Should I normalize/standardize/rescale the data?
      Should I standardize the input variables?
      Should I standardize the target variables?
      Should I standardize the variables for unsupervised learning?
      Should I standardize the input cases?
   Should I nonlinearly transform the data?
   How to measure importance of inputs?
   What is ART?
   What is PNN?
   What is GRNN?
   What does unsupervised learning learn?
   Help! My NN won't learn! What should I do?

Part 3: Generalization
Part 4: Books, data, etc.
Part 5: Free software
Part 6: Commercial software
Part 7: Hardware and miscellaneous

User Contributions:

Report this comment as inappropriate
Apr 24, 2015 @ 7:19 pm
Why is it generally a good idea to omit the biases from the penalty term for weight decay?

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Last Update March 27 2014 @ 02:11 PM