Overview
Access this book
Tax calculation will be finalised at checkout
Other ways to access
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.
Similar content being viewed by others
Keywords
Table of contents (9 chapters)
-
Front Matter
-
Applications
-
Front Matter
-
-
Epilogue
-
Back Matter
Authors and Affiliations
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