HPAVC/HPAVC CD-ROM.iso/NEUMAP3.ZIP/NUMP.ZP/TERMM.HLP

Text File | 1994-09-05 | 7KB | 128 lines

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.