classification - the process of making one of a fixed number of
  possible decisions, given a fixed number of numerical inputs. 
  The output of classification is an integer which indicates 
  the class decision. A network for classifying images of 
  handprinted numerals (0 through 9) would have 10 outputs 
  (in uncoded format). A classifier for processing stock market 
  data could make buy/sell decisions but would not predict
  future prices.

clustering - see unsupervised learning

coded format outputs - in a classification network, coded output 
  format means that the number of outputs is Nout = Log2 (Nc) where 
  Nc is the number of classes. The Nc desired output vectors are 
  then just Nout-bit binary numbers between 0 and Nc-1. 

error function - the function which is minimized during neural net
  training or unsupervised learning. The specific error functions
  minimized in this software package are given in the help files
  for the algorithm in question.

functional link net - A functional link net is a network in which
  (1) nonlinear functions of the inputs are formed to augment or
  add to the input vector, and (2) the outputs are linear functions
  of the augmented input vector. In the most common form of the
  functional link net, the augmented inputs are multinomials 
  formed from the original inputs. Since linear equations can 
  be solved for the output weights, functional link net training 
  is multidimensional polynomial regression. One problem with 
  this type of network is that it suffers from combinatorial 
  explosion. In other words, the number of possible multinomials 
  grows explosively with the network degree.

k-means clustering - given Nc initial clusters, which could come from
  sequential leader clustering, k-means iteratively (1) calculates a new
  mean vector for each cluster (necessary if any input vectors have changed
  clusters) and (2) reclassifies the input vectors to their nearest
  cluster. The sum of the distances between the input vectors and the 
  closest mean vectors is reduced. A distance measure, usually the 
  Euclidean distance, is used. In adaptive k-means, the reclassification 
  and mean calculation steps are performed during one pass through the 
  data. 

layers - An MLP in this software package can have 2 to 4 layers, 
  including the input and output layers. Therefore, a 3-layer network 
  has one hidden layer and a 4-layer network has two hidden layers.

mapping - In mapping, you process numerical inputs into a real-valued 
  (floating point) outputs. A mapper for processing stock market data 
  could predict future prices, but would not make a buy/sell decision.

MSE threshold - one of the two stopping parameters. It is a threshold 
  on the MSE used in training a functional link net or MLP. If the MSE 
  falls below this threshold, training is stopped. To disable this 
  parameter, use a negative value sufor it such as -1.

multilayer perceptron (MLP) - An MLP, sometimes called a backpropagation
  neural network, is a feedforward (usually) network in which outputs
  are algebraic, nonlinear functions of inputs. The MLP has at least
  two layers of units or artificial neurons, the input and output layers. 
  Additional layers, which make the network nonlinear, are called 
  hidden layers.  

network structure file - a file that specifies the structure of 
  a network. For the MLP, this file stores the number of network 
  layers, units per layer, and connectivity between layers.
  For a functional link net, this file specifies the network degree, 
  numbers of inputs and outputs, and the dimension of the multinomial 
  vector.

number of iterations - one of two stopping parameters used in 
  functional link nets and MLPs. This is the maximum number of 
  iterations that can be performed, and is user-chosen.

self-organizing map (SOM) - given Nc initial random clusters, the SOM
  performs an adaptive k-means clustering, except that when a cluster mean
  is updated, its nearest neighbors are also updated. There is a learning 
  factor and a distance threshold which decrease as clustering progresses.

sequential leader (SL) clustering - In SL clustering we are given some
  input vectors, a distance threshold, and one cluster which is the 
  first input vector to be processed. As each subsequent input vector 
  is processed, it is either (1) assigned to the cluster it is closest 
  to, if the distance is below the threshold, or (2) used as the 
  center vector of a new cluster.

standard form - All data files are in standard form, which means that
  the file is formatted, and that each pattern or vector has inputs
  on the left and desired outputs on the right. You can type 
  out the files to examine them, and you can use these files with 
  other neural net software. 

stopping parameters - parameters that specify how training 
  will end. See number of iterations and MSE threshold.

testing data file - the same as a training data file except that 
  (1) it is used to test the performance of a trained network and 
  (2) it may or may not have desired outputs. 

training data file - a formatted file with Nv vectors or patterns.
  Each vector includes N inputs and Nout desired outputs. In 
  classification training data files, the correct class id, which
  is an integer, is stored rather than the Nout desired outputs. 
  See standard form.

training parameters - the learning factor (Z in this software 
  package), and the momentum factor alpha.

uncoded format outputs - in a classification network, uncoded output 
  format means that the number of outputs is Nout = Nc where 
  Nc is the number of classes. The desired output can then be 1 for 
  the correct class and 0 for the others, or 0 for the correct class 
  and 1 for the others (inverted uncoded format). The Nc desired 
  output vectors are then just Nc-bit binary numbers. In the 
  classification network package Neucls.zip, inverted uncoded format 
  and coded format are available.

units - artificial neurons used in the MLP network.

unsupervised learning - Unsupervised learning or clustering is the 
  process of organizing a set of vectors into groups of similar 
  vectors. In many clustering algorithms, each cluster is 
  characterized using a mean or center vector.

weight file - an unformatted file which gives the gains or coefficients
  along paths connecting the various units.

