Abstract
The dimension of the guard zone along with its bounds for the Generalised Learning Algorithm (Pathak and Pal, 1986) is determined for optimum learning. The dimension is found to be dynamic depending on the input sequence and the current estimates of classification parameters. Incorporation of this higher-order knowledge in a supervisory program improves the system performance. The performance is again found to be affected if the guard zone is shrunk/expanded for ‘very weak’/‘not too weak’ estimates when speech data is considered to be input.
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