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Learning from good and bad data

Author: Philip D Laird
Publisher: Boston : Kluwer Academic Publishers, ©1988.
Series: Kluwer international series in engineering and computer science, SECS 47.; Kluwer international series in engineering and computer science., Knowledge representation, learning, and expert systems.
Edition/Format:   Print book : EnglishView all editions and formats
Summary:

This monograph is a contribution to the study of the identification problem: the problem of identifying an item from a known class us- ing positive and negative examples. * Study cognitive models of  Read more...

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Additional Physical Format: Online version:
Laird, Philip D.
Learning from good and bad data.
Boston : Kluwer Academic Publishers, ©1988
(OCoLC)570346807
Online version:
Laird, Philip D.
Learning from good and bad data.
Boston : Kluwer Academic Publishers, ©1988
(OCoLC)607679460
Document Type: Book
All Authors / Contributors: Philip D Laird
ISBN: 0898382637 9780898382631
OCLC Number: 17295644
Notes: Includes index.
Description: xviii, 211 p. : ill. ; 25 cm.
Contents: I Identification in the Limit from Indifferent Teachers.- 1 The Identification Problem.- 1.1 Learning from Indifferent Teachers.- 1.2 A Working Assumption.- 1.3 Convergence.- 1.4 A General Strategy.- 1.5 Examples from Existing Research.- 1.6 Basic Definitions.- 1.7 A General Algorithm.- 1.8 Additional Comments.- 2 Identification by Refinement.- 2.1 Order Homomorphisms.- 2.2 Refinements.- 2.2.1 Introduction.- 2.2.2 Upward and Downward Refinements.- 2.2.3 Summary.- 2.3 Identification by Refinement.- 2.4 Conclusion.- 3 How to Work With Refinements.- 3.1 Introduction.- 3.2 Three Useful Properties.- 3.3 Normal Forms and Monotonic Operations.- 3.4 Universal Refinements.- 3.4.1 Abstract Formulation.- 3.4.2 A Refinement for Clause-Form Sentences.- 3.4.3 Inductive Bias.- 3.5 Conclusions.- 3.6 Appendix to Chapter 3.- 3.6.1 Summary of Logic Notation and Terminology.- 3.6.2 Proof of Theorem 3.32.- 3.6.3 Refinement Properties of Figure 3.2.- II Probabilistic Identification from Random Examples.- 4 Probabilistic Approximate Identification.- 4.1 Probabilistic Identification in the Limit.- 4.2 The Model of Valiant.- 4.2.1 Pac-Identification.- 4.2.2 Identifying Normal-Form Expressions.- 4.2.3 Related Results about Valiant's Model.- 4.3 Using the Partial Order.- 4.4 Summary.- 5 Identification from Noisy Examples.- 5.1 Introduction.- 5.2 Prior Research Results.- 5.3 The Classification Noise Process.- 5.4 Pac-Identification.- 5.4.1 Finite Classes.- 5.4.2 Infinite Classes.- 5.4.3 Estimating the Noise Rate ?.- 5.5 Probabilistic Identification in the Limit.- 5.6 Identifying Normal-Form Expressions.- 5.7 Other Models of Noise.- 5.8 Appendix to Chapter 5.- 6 Conclusions.
Series Title: Kluwer international series in engineering and computer science, SECS 47.; Kluwer international series in engineering and computer science., Knowledge representation, learning, and expert systems.
Responsibility: by Philip D. Laird.
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