H. Osman et Mm. Fahmy, PROBABILISTIC WINNER-TAKE-ALL LEARNING ALGORITHM FOR RADIAL-BASIS-FUNCTION NEURAL CLASSIFIERS, Neural computation, 6(5), 1994, pp. 927-943
This paper proposes a new adaptive competitive learning algorithm call
ed ''the probabilistic winner-take-all.'' The algorithm is based on a
learning scheme developed by Agrawala within the statistical pattern r
ecognition literature (Agrawala 1970). Its name stems from the fact th
at for a given input pattern once each competitor computes the probabi
lity of being the one that generated this pattern, the computed probab
ilities are utilized to probabilistically choose a winner. Then, only
this winner is permitted to learn. The learning rule of the algorithm
is derived for three different cases. Its properties are discussed and
compared to those of two other competitive learning algorithms, namel
y the standard winner-take-all and the maximum-likelihood soft competi
tion. Experimental comparison is also given. When all three algorithms
are used to train the hidden layer of radial-basis-function classifie
rs, experiments indicate that classifiers trained with the probabilist
ic winner-take-all outperform those trained with the other two algorit
hms.