UID:
almafu_9959327082002883
Format:
1 online resource (1 volume) :
,
illustrations
ISBN:
9781118023471
,
1118023471
,
9781118023433
,
1118023439
,
1283098687
,
9781283098687
Content:
"A joint endeavor from leading researchers in the fields of philosophy and electrical engineering An Introduction to Statistical Learning Theory provides a broad and accessible introduction to rapidly evolving field of statistical pattern recognition and statistical learning theory. Exploring topics that are not often covered in introductory level books on statistical learning theory, including PAC learning, VC dimension, and simplicity, the authors present upper-undergraduate and graduate levels with the basic theory behind contemporary machine learning and uniquely suggest it serves as an excellent framework for philosophical thinking about inductive inference"--Back cover.
Note:
Introduction: Classification, Learning, Features, and Applications -- Probability -- Probability Densities -- The Pattern Recognition Problem -- The Optimal Bayes Decision Rule -- Learning from Examples -- The Nearest Neighbor Rule -- Kernel Rules -- Neural Networks: Perceptrons -- Multilayer Networks -- PAC Learning -- VC Dimension -- Infinite VC Dimension -- The Function Estimation Problem -- Learning Function Estimation -- Simplicity -- Support Vector Machines -- Boosting.
Additional Edition:
Print version: Kulkarni, Sanjeev. Elementary introduction to statistical learning theory. Hoboken, N.J. : Wiley, ©2011 ISBN 9781118023471
Language:
English
Subjects:
Computer Science
Keywords:
Llibres electrònics.
;
Electronic books.
;
Llibres electrònics.
;
Electronic books.
;
Llibres electrònics.
;
Electronic books.
URL:
https://onlinelibrary.wiley.com/doi/book/10.1002/9781118023471
URL:
https://onlinelibrary.wiley.com/doi/book/10.1002/9781118023471
URL:
https://onlinelibrary.wiley.com/doi/book/10.1002/9781118023471
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