On Utilizing Stochastic Learning Weak Estimators for Training and Classification of Patterns with Non-stationary Distributions

Pattern recognition essentially deals with the training and classification of patterns, where the distribution of the features is assumed unknown. However, in almost all the reported results, a fundamental assumption made is that this distribution, although unknown, is stationary. In this paper, we shall relax this assumption and assume that the class-conditional distribution is non-stationary. To now render the training and classification feasible, we present a novel estimation strategy, involving the so-called Stochastic Learning Weak Estimator (SLWE). The latter, whose convergence is weak, is used to estimate the parameters of a binomial distribution using the principles of stochastic learning. Even though our method includes a learning coefficient, λ, it turns out that the mean of the final estimate is independent of λ, the variance of the final distribution decreases with λ, and the speed decreases with λ. Similar results are true for the multinomial case. To demonstrate the power of these estimates in data which is truly “nonstationary”, we have used them in 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.