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Error estimation for pattern recognition / Ulisses M. Braga Neto and Edward R. Dougherty.

By: Contributor(s): Publication details: New Jersey : John Wiley, c2015.Description: xxii, 311 p. : illustrations ; 24 cmISBN:
  • 9781118999738
Subject(s): DDC classification:
  • 006.4 23 B813
Contents:
1. Classification -- 2. Error Estimation -- 3. Performance Analysis -- 4. Error Estimation for Discrete Classification -- 5. Distribution Theory -- 6. Gaussian Distribution Theory: Univariate Case -- 7. Gaussian Distribution Theory: Multivariate Case -- 8. Bayesian MMSE Error Estimation -- A. Basic Probability Review -- B. Vapnik-Chervonenkis Theory -- C. Double Asymptotics -- Bibliography -- Author index -- Subject index.
Summary: This book is the first of its kind to discuss error estimation with a model-based approach. From the basics of classifiers and error estimators to more specialized classifiers, it covers important topics and essential issues pertaining to the scientific validity of pattern classification. Includes the latest results on accuracy of error estimation Analyzes the performance of cross-validation and bootstrap error estimators using simulation and model-based approaches End-of-chapter exercises Highly interactive computer-based exercises.
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Holdings
Item type Current library Call number Status Date due Barcode Item holds
Books ISI Library, Kolkata 006.4 B813 (Browse shelf(Opens below)) Available 136817
Total holds: 0

Includes bibliographical references and indexes.

1. Classification --
2. Error Estimation --
3. Performance Analysis --
4. Error Estimation for Discrete Classification --
5. Distribution Theory --
6. Gaussian Distribution Theory: Univariate Case --
7. Gaussian Distribution Theory: Multivariate Case --
8. Bayesian MMSE Error Estimation --
A. Basic Probability Review --
B. Vapnik-Chervonenkis Theory --
C. Double Asymptotics --
Bibliography --
Author index --
Subject index.

This book is the first of its kind to discuss error estimation with a model-based approach. From the basics of classifiers and error estimators to more specialized classifiers, it covers important topics and essential issues pertaining to the scientific validity of pattern classification. Includes the latest results on accuracy of error estimation Analyzes the performance of cross-validation and bootstrap error estimators using simulation and model-based approaches End-of-chapter exercises Highly interactive computer-based exercises.

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