Classification -- Error Estimation -- Performance Analysis -- Error Estimation for Discrete Classification -- Distribution Theory -- Gaussian Distribution Theory: Univariate Case -- Gaussian Distribution Theory: Multivariate Case -- Bayesian MMSE Error Estimation -- Basic Probability Review -- Vapnik-Chervonenkis Theory -- Double Asymptotics
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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