B. John Oommen

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The fastest learning automata (LA) algorithms currently available fall in the family of estimator algorithms introduced by Thathachar and Sastry (1986). The pioneering work of these authors was the pursuit algorithm, which pursues only the current estimated optimal action. If this action is not the one with the minimum penalty probability, this algorithm(More)
In this paper we introduce a new self-organizing neural network, the Kohonen Network Incorporating Explicit Statistics (KNIES) that is based on Kohonen's Self-Organizing Map (SOM). The primary difference between the SOM and the KNIES is the fact that every iteration in the training phase includes two distinct modules-the attracting module and the dispersing(More)
A learning automaton (LA) is an automaton that interacts with a random environment, having as its goal the task of learning the optimal action based on its acquired experience. Many learning automata (LAs) have been proposed, with the class of estimator algorithms being among the fastest ones, Thathachar and Sastry, through the pursuit algorithm, introduced(More)
In this paper, we formally present a novel estimation method, referred to as the Stochastic Learning Weak Estimator (SLWE), which yields the estimate of the parameters of a binomial distribution, where the convergence of the estimate is weak, i.e. with regard to the rst and second moments. The estimation is based on the principles of stochastic learning.(More)
BOUNDED MEMORY PROBABILISTIC MOVE-TO-FRONT OPERATIONS The concept of performing probabilistic move operations on an accessed element is not entirely new. Kan and Ross [9] suggested a probabilistic transposition scheme and showed that no advantage was obtained by rendering the scheme probabilistic. Their scheme, however, required that the probability of(More)
Various Prototype Reduction Schemes (PRS) have been reported in the literature. Based on their operating characteristics, these schemes fall into two fairly distinct categories — those which are of a creative sort, and those which are essentially selective. The norms for evaluating these methods are typically, the reduction rate and the classification(More)
We consider the problem of a learning mechanism (for example, a robot) locating a point on a line when it is interacting with a random environment which essentially informs it, possibly erroneously, which way it should move. In this paper we present a novel scheme by which the point can he learned using some recently devised learning principles. The heart(More)
In this paper we present a foundational basis for optimal and information theoretic syntactic pattern recognition. We do this by developing a rigorous model, M*, for channels which permit arbitrarily distributed substitution, deletion and insertion syntactic errors. More explicitly, if A is any finite alphabet and A* the set of words over A, we specify a(More)