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Learning Algorithms Theory and Applications

Theory and Applications

  • Textbook
  • © 1981

Overview

Authors:
  1. S. Lakshmivarahan

    1. School of Electrical Engineering and Computer Science, University of Oklahoma, Norman, USA

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Table of contents (9 chapters)

  1. Front Matter

    Pages i-x
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  2. Theory

    1. Front Matter

      Pages N1-N1
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    2. Introduction

      • S. Lakshmivarahan
      Pages 1-18

    3. Ergodic Learning Algorithms

      • S. Lakshmivarahan
      Pages 19-65

    4. Absolutely Expedient Algorithms

      • S. Lakshmivarahan
      Pages 66-108

    5. Time Varying Learning Algorithms

      • S. Lakshmivarahan
      Pages 109-135

  4. Epilogue

    1. Epilogue

      • S. Lakshmivarahan
      Pages 257-258

  5. Back Matter

    Pages 259-279
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Keywords

About this book

Learning constitutes one of the most important phase of the whole psychological processes and it is essential in many ways for the occurrence of necessary changes in the behavior of adjusting organisms. In a broad sense influence of prior behavior and its consequence upon subsequent behavior is usually accepted as a definition of learning. Till recently learning was regarded as the prerogative of living beings. But in the past few decades there have been attempts to construct learning machines or systems with considerable success. This book deals with a powerful class of learning algorithms that have been developed over the past two decades in the context of learning systems modelled by finite state probabilistic automaton. These algorithms are very simple iterative schemes. Mathematically these algorithms define two distinct classes of Markov processes with unit simplex (of suitable dimension) as its state space. The basic problem of learning is viewed as one of finding conditions on the algorithm such that the associated Markov process has prespecified asymptotic behavior. As a prerequisite a first course in analysis and stochastic processes would be an adequate preparation to pursue the development in various chapters.

Authors and Affiliations

  • School of Electrical Engineering and Computer Science, University of Oklahoma, Norman, USA

    S. Lakshmivarahan

Bibliographic Information

  • Book Title: Learning Algorithms Theory and Applications

  • Book Subtitle: Theory and Applications

  • Authors: S. Lakshmivarahan

  • DOI: https://doi.org/10.1007/978-1-4612-5975-6

  • Publisher: Springer New York, NY

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer-Verlag New York Inc. 1981

  • Softcover ISBN: 978-0-387-90640-9Published: 02 November 1981

  • eBook ISBN: 978-1-4612-5975-6Published: 06 December 2012

  • Edition Number: 1

  • Number of Pages: XII, 280

  • Topics: Numerical Analysis

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