Robustness in Statistical Pattern Recognition by Y. KharinRobustness in Statistical Pattern Recognition by Y. Kharin

Robustness in Statistical Pattern Recognition

byY. Kharin

Paperback | December 15, 2010

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This monograph is devoted to problems of robust (stable) statistical pattern recognition. Experimental data to be classified usually deviate from assumed hypothetical probability models of the data. In such cases traditional decision rules constructed by means of the classical pattern recognition theory based on a fixed hypothetical model of the data often become non-stable, and the classification risk increases non-controllably. The book concentrates on three main problems: robustness evaluation for classical decision rules in the presence of distortion; estimation of critical levels of distortions for given values of the robustness factor; and the construction of robust decision rules with stable classification risk regarding certain types of distortions. Theoretical results are illustrated by computer modelling and by application to medical diagnostics. Audience: This volume is primarily intended for mathematicians, statisticians, and engineers in applied mathematics, computer science and cybernetics. It is also recommended as a textbook for a one-semester course for advanced undergraduate and graduate students training in the indicated fields.
Title:Robustness in Statistical Pattern RecognitionFormat:PaperbackDimensions:316 pages, 11.69 × 8.27 × 0.03 inPublished:December 15, 2010Publisher:Springer NetherlandsLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:9048147603

ISBN - 13:9789048147601

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Table of Contents

Preface. 1. Probability Models of Data and Optimal Decision Rules. 2. Violations of Model Assumptions and Basic Notions in Decision Rule Robustness. 3. Robustness of Parametric Decision Rules and Small-Sample Effects. 4. Robustness of Nonparametric Decision Rules and Small-Sample Effects. 5. Decision Rule Robustness under Distortions of Observations to be Classified. 6. Decision Rule Robustness under Distortions of Training Samples. 7. Cluster Analysis under Distorted Model Assumptions. Bibliography. Index. Main Notations and Abbreviations.