(1) Statement: Classify input vowel patterns into |
two category of vowel, VOWEL-A and VOWEL-B |
(2) Initialize: Smoothing parameter |
(Determined from observation of successful learning) |
(3) Output of each pattern unit, |
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( is the weight vector) |
(4) Find neuron activation function by performing non-linear operation of the form |
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(5) Sum all the for category VOWEL-A and do the same for category VOWEL-B |
(6) Take binary decision for the two summation outputs with variable weight given by- |
|
where, |
and Priori probability of occurrence of pattern from VOWEL-A and VOWEL-B respectively |
Loss associated with wrong decision |
and No of patterns in |
VOWEL-A and VOWEL-B respectively which is 10 for both categories |