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comp.ai.neural-nets FAQ, Part 1 of 7: Introduction
Section - How many kinds of NNs exist?

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There are many many kinds of NNs by now. Nobody knows exactly how many. New
ones (or at least variations of old ones) are invented every week. Below is
a collection of some of the most well known methods, not claiming to be
complete.

The two main kinds of learning algorithms are supervised and unsupervised. 

 o In supervised learning, the correct results (target values, desired
   outputs) are known and are given to the NN during training so that the NN
   can adjust its weights to try match its outputs to the target values.
   After training, the NN is tested by giving it only input values, not
   target values, and seeing how close it comes to outputting the correct
   target values. 
 o In unsupervised learning, the NN is not provided with the correct results
   during training. Unsupervised NNs usually perform some kind of data
   compression, such as dimensionality reduction or clustering. See "What
   does unsupervised learning learn?" 

The distinction between supervised and unsupervised methods is not always
clear-cut. An unsupervised method can learn a summary of a probability
distribution, then that summarized distribution can be used to make
predictions. Furthermore, supervised methods come in two subvarieties:
auto-associative and hetero-associative. In auto-associative learning, the
target values are the same as the inputs, whereas in hetero-associative
learning, the targets are generally different from the inputs. Many
unsupervised methods are equivalent to auto-associative supervised methods.
For more details, see "What does unsupervised learning learn?" 

Two major kinds of network topology are feedforward and feedback. 

 o In a feedforward NN, the connections between units do not form cycles.
   Feedforward NNs usually produce a response to an input quickly. Most
   feedforward NNs can be trained using a wide variety of efficient
   conventional numerical methods (e.g. see "What are conjugate gradients,
   Levenberg-Marquardt, etc.?") in addition to algorithms invented by NN
   reserachers. 
 o In a feedback or recurrent NN, there are cycles in the connections. In
   some feedback NNs, each time an input is presented, the NN must iterate
   for a potentially long time before it produces a response. Feedback NNs
   are usually more difficult to train than feedforward NNs. 

Some kinds of NNs (such as those with winner-take-all units) can be
implemented as either feedforward or feedback networks. 

NNs also differ in the kinds of data they accept. Two major kinds of data
are categorical and quantitative. 

 o Categorical variables take only a finite (technically, countable) number
   of possible values, and there are usually several or more cases falling
   into each category. Categorical variables may have symbolic values (e.g.,
   "male" and "female", or "red", "green" and "blue") that must be encoded
   into numbers before being given to the network (see "How should
   categories be encoded?") Both supervised learning with categorical target
   values and unsupervised learning with categorical outputs are called
   "classification." 
 o Quantitative variables are numerical measurements of some attribute, such
   as length in meters. The measurements must be made in such a way that at
   least some arithmetic relations among the measurements reflect analogous
   relations among the attributes of the objects that are measured. For more
   information on measurement theory, see the Measurement Theory FAQ at 
   ftp://ftp.sas.com/pub/neural/measurement.html. Supervised learning with
   quantitative target values is called "regression." 

Some variables can be treated as either categorical or quantitative, such as
number of children or any binary variable. Most regression algorithms can
also be used for supervised classification by encoding categorical target
values as 0/1 binary variables and using those binary variables as target
values for the regression algorithm. The outputs of the network are
posterior probabilities when any of the most common training methods are
used. 

Here are some well-known kinds of NNs: 

1. Supervised 

   1. Feedforward 

       o Linear 
          o Hebbian - Hebb (1949), Fausett (1994) 
          o Perceptron - Rosenblatt (1958), Minsky and Papert (1969/1988),
            Fausett (1994) 
          o Adaline - Widrow and Hoff (1960), Fausett (1994) 
          o Higher Order - Bishop (1995) 
          o Functional Link - Pao (1989) 
       o MLP: Multilayer perceptron - Bishop (1995), Reed and Marks (1999),
         Fausett (1994) 
          o Backprop - Rumelhart, Hinton, and Williams (1986) 
          o Cascade Correlation - Fahlman and Lebiere (1990), Fausett (1994)
          o Quickprop - Fahlman (1989) 
          o RPROP - Riedmiller and Braun (1993) 
       o RBF networks - Bishop (1995), Moody and Darken (1989), Orr (1996) 
          o OLS: Orthogonal Least Squares - Chen, Cowan and Grant (1991) 
       o CMAC: Cerebellar Model Articulation Controller - Albus (1975),
         Brown and Harris (1994) 
       o Classification only 
          o LVQ: Learning Vector Quantization - Kohonen (1988), Fausett
            (1994) 
          o PNN: Probabilistic Neural Network - Specht (1990), Masters
            (1993), Hand (1982), Fausett (1994) 
       o Regression only 
          o GNN: General Regression Neural Network - Specht (1991), Nadaraya
            (1964), Watson (1964) 

   2. Feedback - Hertz, Krogh, and Palmer (1991), Medsker and Jain (2000)

       o BAM: Bidirectional Associative Memory - Kosko (1992), Fausett
         (1994) 
       o Boltzman Machine - Ackley et al. (1985), Fausett (1994) 
       o Recurrent time series 
          o Backpropagation through time - Werbos (1990) 
          o Elman - Elman (1990) 
          o FIR: Finite Impulse Response - Wan (1990) 
          o Jordan - Jordan (1986) 
          o Real-time recurrent network - Williams and Zipser (1989) 
          o Recurrent backpropagation - Pineda (1989), Fausett (1994) 
          o TDNN: Time Delay NN - Lang, Waibel and Hinton (1990) 

   3. Competitive 

       o ARTMAP - Carpenter, Grossberg and Reynolds (1991) 
       o Fuzzy ARTMAP - Carpenter, Grossberg, Markuzon, Reynolds and Rosen
         (1992), Kasuba (1993) 
       o Gaussian ARTMAP - Williamson (1995) 
       o Counterpropagation - Hecht-Nielsen (1987; 1988; 1990), Fausett
         (1994) 
       o Neocognitron - Fukushima, Miyake, and Ito (1983), Fukushima,
         (1988), Fausett (1994) 

2. Unsupervised - Hertz, Krogh, and Palmer (1991) 

   1. Competitive 

       o Vector Quantization 
          o Grossberg - Grossberg (1976) 
          o Kohonen - Kohonen (1984) 
          o Conscience - Desieno (1988) 
       o Self-Organizing Map 
          o Kohonen - Kohonen (1995), Fausett (1994) 
          o GTM: - Bishop, SvensÚn and Williams (1997) 
          o Local Linear - Mulier and Cherkassky (1995) 
       o Adaptive resonance theory 
          o ART 1 - Carpenter and Grossberg (1987a), Moore (1988), Fausett
            (1994) 
          o ART 2 - Carpenter and Grossberg (1987b), Fausett (1994) 
          o ART 2-A - Carpenter, Grossberg and Rosen (1991a) 
          o ART 3 - Carpenter and Grossberg (1990) 
          o Fuzzy ART - Carpenter, Grossberg and Rosen (1991b) 
       o DCL: Differential Competitive Learning - Kosko (1992) 

   2. Dimension Reduction - Diamantaras and Kung (1996) 

       o Hebbian - Hebb (1949), Fausett (1994) 
       o Oja - Oja (1989) 
       o Sanger - Sanger (1989) 
       o Differential Hebbian - Kosko (1992) 

   3. Autoassociation 

       o Linear autoassociator - Anderson et al. (1977), Fausett (1994) 
       o BSB: Brain State in a Box - Anderson et al. (1977), Fausett (1994) 
       o Hopfield - Hopfield (1982), Fausett (1994) 

3. Nonlearning 

   1. Hopfield - Hertz, Krogh, and Palmer (1991) 
   2. various networks for optimization - Cichocki and Unbehauen (1993) 

References: 

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   Anderson, J.A., and Rosenfeld, E., eds. (1988), Neurocomputing:
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