+612 9045 4394
 
CHECKOUT
Learning from Good and Bad Data : Springer International Series in Engineering and Computer Sc - Philip D. Laird

Learning from Good and Bad Data

Springer International Series in Engineering and Computer Sc

Hardcover

Published: December 2009
Ships: 7 to 10 business days
7 to 10 business days
RRP $485.99
$335.95
31%
OFF
or 4 easy payments of $83.99 with Learn more

Other Available Formats (Hide)

  • Paperback View Product Published: 5th October 2011
    $213.24

Advertising is becoming more international. Advertising campaigns increasingly use similar themes or even the same advertisements in more than one country. Consequently, advertisers and their attorneys need to be aware of the advertising law in more than one jurisdiction.

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.

ISBN: 9780898382631
ISBN-10: 0898382637
Series: Springer International Series in Engineering and Computer Sc
Audience: Professional
Format: Hardcover
Language: English
Number Of Pages: 212
Published: December 2009
Publisher: SPRINGER VERLAG GMBH
Country of Publication: US
Dimensions (cm): 23.39 x 15.6  x 1.42
Weight (kg): 0.5