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- M. Agache, B. John Oommen
- IEEE Trans. Systems, Man, and Cybernetics, Part B
- 2002

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 algorithmâ€¦ (More)

- B. John Oommen, J. Kevin LanctÃ´t
- IEEE Trans. Systems, Man, and Cybernetics
- 1990

- Necati Aras, B. John Oommen, I. Kuban Altinel
- Neural Networks
- 1999

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 dispersingâ€¦ (More)

- B. John Oommen, M. Agache
- IEEE Trans. Systems, Man, and Cybernetics, Part B
- 2001

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, introducedâ€¦ (More)

- B. John Oommen, Luis Rueda
- Pattern Recognition
- 2006

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.â€¦ (More)

- B. John Oommen, Daniel C. Y. Ma
- IEEE Trans. Computers
- 1988

- J. Kevin LanctÃ´t, B. John Oommen
- IEEE Trans. Systems, Man, and Cybernetics
- 1992

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 ofâ€¦ (More)

- Sang-Woon Kim, B. John Oommen
- Pattern Analysis & Applications
- 2003

Various Prototype Reduction Schemes (PRS) have been reported in the literature. Based on their operating characteristics, these schemes fall into two fairly distinct categories â€” 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 classificationâ€¦ (More)

- B. John Oommen
- IEEE Trans. Systems, Man, and Cybernetics, Part B
- 1997

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 heartâ€¦ (More)

- B. John Oommen, Rangasami L. Kashyap
- Pattern Recognition
- 1998

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 aâ€¦ (More)