Learning with Nested Generalized Exemplars

  • Steven L. Salzberg

Part of the The Kluwer International Series in Engineering and Computer Science book series (SECS, volume 100)

Table of contents

  1. Front Matter
    Pages i-xix
  2. Steven L. Salzberg
    Pages 1-26
  3. Steven L. Salzberg
    Pages 27-53
  4. Steven L. Salzberg
    Pages 55-78
  5. Steven L. Salzberg
    Pages 79-113
  6. Steven L. Salzberg
    Pages 115-123
  7. Back Matter
    Pages 125-159

About this book

Introduction

Machine Learning is one of the oldest and most intriguing areas of Ar­ tificial Intelligence. From the moment that computer visionaries first began to conceive the potential for general-purpose symbolic computa­ tion, the concept of a machine that could learn by itself has been an ever present goal. Today, although there have been many implemented com­ puter programs that can be said to learn, we are still far from achieving the lofty visions of self-organizing automata that spring to mind when we think of machine learning. We have established some base camps and scaled some of the foothills of this epic intellectual adventure, but we are still far from the lofty peaks that the imagination conjures up. Nevertheless, a solid foundation of theory and technique has begun to develop around a variety of specialized learning tasks. Such tasks in­ clude discovery of optimal or effective parameter settings for controlling processes, automatic acquisition or refinement of rules for controlling behavior in rule-driven systems, and automatic classification and di­ agnosis of items on the basis of their features. Contributions include algorithms for optimal parameter estimation, feedback and adaptation algorithms, strategies for credit/blame assignment, techniques for rule and category acquisition, theoretical results dealing with learnability of various classes by formal automata, and empirical investigations of the abilities of many different learning algorithms in a diversity of applica­ tion areas.

Keywords

algorithms artificial intelligence automatic classification behavior classification cluster analysis complexity computer vision control intelligence knowledge representation learning machine learning natural language simulation

Authors and affiliations

  • Steven L. Salzberg
    • 1
  1. 1.The Johns Hopkins UniversityUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4613-1549-0
  • Copyright Information Springer-Verlag US 1990
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4612-8830-5
  • Online ISBN 978-1-4613-1549-0
  • Series Print ISSN 0893-3405
  • About this book
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