| (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, |
|
| ( is the weight vector) |
| (4) Find neuron activation function by performing non-linear operation of the form |
| |
| (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 |