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Annual Conference on Artificial Intelligence

KI 2005: KI 2005: Advances in Artificial Intelligence pp 107–120Cite as

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

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3698))

Abstract

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 “non-stationary”, 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.

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Author information

Authors and Affiliations

  1. School of Computer Science, Carleton University, 1125 Colonel By Dr., Ottawa, ON, K1S 5B6, Canada

    B. John Oommen

  2. School of Computer Science, University of Windsor, 401 Sunset Avenue, Windsor, ON, N9B 3P4, Canada

    Luis Rueda

Authors
  1. B. John Oommen
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  2. Luis Rueda
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Editor information

Editors and Affiliations

  1. Department of Computer Science, Artificial Intelligence Research Group, University of Koblenz-Landau,, Universitätsstr. 1, 56070, Koblenz

    Ulrich Furbach

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© 2005 Springer-Verlag Berlin Heidelberg

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Oommen, B.J., Rueda, L. (2005). On Utilizing Stochastic Learning Weak Estimators for Training and Classification of Patterns with Non-stationary Distributions. In: Furbach, U. (eds) KI 2005: Advances in Artificial Intelligence. KI 2005. Lecture Notes in Computer Science(), vol 3698. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11551263_10

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  • DOI: https://doi.org/10.1007/11551263_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28761-2

  • Online ISBN: 978-3-540-31818-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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