This class implements either a:
Probabilistic Neural Network (PNN)
General Regression Neural Network (GRNN)
To use a PNN specify an output mode of classification, to make use of a GRNN
specify either an output mode of regression or un-supervised autoassociation.
The forward-backward algorithm is an inference algorithm for hidden Markov
models which computes the posterior marginals of all hidden state variables
given a sequence of observations.
The forward-backward algorithm is an inference algorithm for hidden Markov
models which computes the posterior marginals of all hidden state variables
given a sequence of observations.
A Hidden Markov Model (HMM) is a Machine Learning Method that allows for
predictions to be made about the hidden states and observations of a given
system over time.
Create a bubble neighborhood function that will return 1.0 (full update)
for any neuron that is plus or minus the width distance from the winning
neuron.
A radial basis function (RBF) network uses several radial basis functions to
provide a more dynamic hidden layer activation function than many other types
of neural network.
Set the number of output neurons, should not be used with a hopfield
neural network, because the number of input neurons defines the number of
output neurons.
The Viterbi algorithm is used to find the most likely sequence of hidden
states (called the Viterbi path) that results in a sequence of observed
events.