Hunt and Black algorithm

Figure 16.1 Diagram of Hunt and Black algorithm, showing one particular sequence of units and how the target cost measures a distance between a unit and the specification, and how the join cost measures a distance between the two adjacent units.

Various ways of setting the weights automatically have been proposed. Since the Hunt and Black algorithm is not probabihstic, we cannot directly use the most-standard probabilistic training technique of maximum likelihood. We can, however, investigate similar types of techniques that try to set the parameters so as to generate data that are closest to a set of training data. 

Recall that in the Hunt and Black algorithm (Equations (16.2) and (16.3)) the total cost of one sequence of units from the database U = u, U2. v) with the specification... 

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