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Learning and generalisation : with applications to neural networks 2nd ed

Learning and generalisation : with applications to neural networks 2nd ed (Loan 2 times)

Material type
단행본
Personal Author
Vidyasagar, M. (Mathukumalli) , 1947-.
Title Statement
Learning and generalisation : with applications to neural networks / M. Vidyasagar.
판사항
2nd ed.
Publication, Distribution, etc
London ;   New York :   Springer ,   c2003.  
Physical Medium
xxi, 488 p. : ill. ; 25 cm.
Series Statement
Communications and control engineering , 0178-5354
ISBN
1852333731 (alk. paper)
Bibliography, Etc. Note
Includes bibliographical references (p. [475]-484) and index.
Subject Added Entry-Topical Term
Machine learning. Control theory. Neural networks (Computer science)
000 01011pamuu2200289 a 4500
001 000045221396
005 20060227164336
008 020515s2003 enka b 001 0 eng
010 ▼a 2002070674
020 ▼a 1852333731 (alk. paper)
035 ▼a (KERIS)REF000006507884
040 ▼a DLC ▼c DLC ▼d DLC ▼d 211009
050 0 0 ▼a Q325.5 ▼b .V53 2003
082 0 0 ▼a 006.3/1 ▼2 21
090 ▼a 006.31 ▼b V655L2
100 1 ▼a Vidyasagar, M. ▼q (Mathukumalli) , ▼d 1947-.
245 1 0 ▼a Learning and generalisation : ▼b with applications to neural networks / ▼c M. Vidyasagar.
250 ▼a 2nd ed.
260 ▼a London ; ▼a New York : ▼b Springer , ▼c c2003.
300 ▼a xxi, 488 p. : ▼b ill. ; ▼c 25 cm.
440 0 ▼a Communications and control engineering , ▼x 0178-5354
504 ▼a Includes bibliographical references (p. [475]-484) and index.
650 0 ▼a Machine learning.
650 0 ▼a Control theory.
650 0 ▼a Neural networks (Computer science)
945 ▼a KINS

Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Science & Engineering Library/Sci-Info(Stacks2)/ Call Number 006.31 V655L2 Accession No. 121121344 Availability Available Due Date Make a Reservation Service B M

Contents information

Table of Contents

1. Introduction.- 2. Preliminaries.- 3. Problem Formulations.- 4. Vapnik-Chervonenkis, Pseudo- and Fat-Shattering Dimensions.- 5. Uniform Convergence of Empirical Means.- 6. Learning Under a Fixed Probability Measure.- 7. Distribution-Free Learning.- 8. Learning Under an Intermediate Family of Probabilities.- 9. Alternate Models of Learning.- 10. Applications to Neural Networks..- 11. Applications to Control Systems.- 12. Some Open Problems.


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