B. John Oommen

A brief taxonomy and ranking of creative prototype reduction schemes

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 accuracy. It is generally believed that the former class of methods is superior to the latter. In this paper, we report the results of executing various creative PRSs, and attempt to comparatively quantify their capabilities. The paper presents a brief taxonomy of the various reported PRS schemes. Our experimental results for three artificial data sets, and for samples involvingreal-life data sets, demonstrate that no single method is uniformly superior to the others for all kinds of applications. This result, though consistent with the findings of Bezdek and Kuncheva [1], is, in one sense, counter-intuitive, because the various researchers have presented their specific PRS with the hope that it would be superior to the previously reported methods. However, the fact is that while one method is superior in certain domains, it is inferior to another method when dealing with a data set with markedly different characteristics. The conclusion of this study is that the question of determining when one method is superior to another remains open. Indeed, it appears as if the designers of the pattern recognition system will have to choose the appropriate PRS based to the specific characteristics of the data that they are studying. The paper also suggests answers to various hypotheses that relate to the accuracies and reduction rates of families of PRS.

Stochastic learning-based weak estimation of multinomial random variables and its applications to pattern recognition in non-stationary environments

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 first and second moments. The estimation is based on the principles of stochastic learning. The mean of the final estimate is independent of the schemes learning coefficient, @l, and both the variance of the final distribution and the speed decrease with @l. Similar results are true for the multinomial case, except that the equations transform from being of a scalar type to be of a vector type. Amazingly enough, the speed of the latter only depends on the same parameter, @l, which turns out to be the only non-unity eigenvalue of the underlying stochastic matrix that determines the time-dependence of the estimates. An empirical analysis on synthetic data shows the advantages of the scheme for non-stationary distributions. The paper also briefly reports (without detailed explanation) conclusive results that demonstrate the superiority of SLWE in pattern-recognition-based data compression, where the underlying data distribution is non-stationary. Finally, and more importantly, the paper includes the results of two pattern recognition exercises, the first of which involves artificial data, and the second which involves the recognition of the types of data that are present in news reports of the Canadian Broadcasting Corporation (CBC). The superiority of the SLWE in both these cases is demonstrated.

Recognition of Noisy Subsequences Using Constrained Edit Distances

Let X* be any unknown word from a finite dictionary H. Let U be any arbitrary subsequence of X*. We consider the problem of estimating X* by processing Y, which is a noisy version of U. We do this by defining the constrained edit distance between XH and Y subject to any arbitrary edit constraint involving the number and type of edit operations to be performed. An algorithm to compute this constrained edit distance has been presented. Although in general the algorithm has a cubic time complexity, within the framework of our solution the algorithm possesses a quadratic time complexity. Recognition using the constrained edit distance as a criterion demonstrates remarkable accuracy. Experimental results which involve strings of lengths between 40 and 80 and which contain an average of 26.547 errors per string demonstrate that the scheme has about 99.5 percent accuracy.

List organizing strategies using stochastic move-to-front and stochastic move-to-rear operations

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Cybernetics and Learning Automata

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On using prototype reduction schemes to optimize kernel-based nonlinear subspace methods

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Service selection in stochastic environments: a learning-automaton based solution

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A novel strategy for solving the stochastic point location problem using a hierarchical searching scheme.

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String alignment with substitution, insertion, deletion, squashing, and expansion operations

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Topology-oriented self-organizing maps: a survey

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