UID:
almahu_9947367283102882
Format:
1 online resource (221 p.)
ISBN:
1-282-28936-5
,
9786612289361
,
0-08-095610-6
Series Statement:
Mathematics in science and engineering ; v. 101
Uniform Title:
Osnovy teorii obuchai͡ushchikhsi͡a sistem.
Content:
Foundations of the theory of learning systems
Note:
Translation of Osnovy teorii obuchaiushchikhsia sistem.
,
Front Cover; Foundations of the Theory of Learning Systems; Copyright Page; Contents; Preface to the Russian Edition; Acknowledgments; Chapter I. Goal of Learning; 1.1 Introduction; 1.2 Concept of the Goal of Learning; 1.3 Complex Goals of Learning; 1.4 Constraints; 1.5 Types of Learning; 1.6 Discussion; 1.7 Conclusion; Comments; References; Chapter II. Algorithms of Learning; 2.1 Introduction; 2.2 Algorithmic Approach; 2.3 Algorithms of Learning; 2.4 Convergence of the Algorithms; 2.5 Criterion of Convergence of the Algorithms; 2.6 Modified Algorithms; 2.7 General Algorithms of Learning
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2.8 Special Cases2.9 Algorithms of Learning in the Presence of Constraints; 2.10 Special Cases; 2.11 Conclusion; Comments; References; Chapter III. Algorithms of Optimal Learning; 3.1 Introduction; 3.2 Performance Indices of Learning; 3.3 Generalized Performance Indices of Learning; 3.4 Discrete Algorithms of Quasi-Optimal Learning; 3.5 Linear Discrete Algorithms of Optimal Learning: I; 3.6 Linear Discrete Algorithms of Optimal Learning: II; 3.7 Discussion; 3.8 The Simplest Linear Discrete Algorithms of Optimal Learning; 3.9 More on Discrete Linear Algorithms of Optimal Learning
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3.10 Continuous and Hybrid Algorithms of Optimal Learning3.11 Special Cases; 3.12 More on Continuous Algorithms of Optimal Learning; 3.13 The Limiting Case; 3.14 Discussion; 3.15 Algorithms of Learning with Repetition; 3.16 Conclusion; Comments; References; Chapter IV. Elements of Statistical Decision Theory; 4.1 Introduction; 4.2 Average Risk; 4.3 Conditions for the Minimum of the AverageRisk; 4.4 Binary Case; 4.5 Classical Bayes Approach; 4.6 Siegert-Kotelnikov Rule; 4.7 Maximum A Posteriori Probability Rule; 4.8 Mixed Decision Rule; 4.9 Neyman-Pearson Rule; 4.10 Min-Max Decision Rule
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4.11 General Decision Rule4.12 Discussion; 4.13 Conclusion; Comments; References; Chapter V. Learning Pattern Recognition Systems; 5.1 Introduction; 5.2 Goal of Learning; 5.3 Binary Case; 5.4 Traditional Adaptive Approach; 5.5 Adaptive Bayes Approach: I; 5.6 Adaptive Bayes Approach: II; 5.7 Learning to Apply the Siegert-Kotelnikov Rule; 5.8 Learning to Apply the Mixed Decision Rule; 5.9 Learning to Apply the Neyman-Pearson Rule; 5.10 Is It Necessary to Learn the Min-Max Decision Rule?; 5.11 Learning to Apply the General Decision Rule; 5.12 Discussion; 5.13 Conclusion; Comments; References
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Chapter VI. Self-Learning Systems of Classification6.1 Introduction; 6.2 Goal of Self-Learning; 6.3 Binary Case; 6.4 Algorithms of Self-Learning; 6.5 Algorithms of Optimal Self-Learning; 6.6 Adaptive Bayes Approach; 6.7 Self-Learning When the Number of Regions Is Known; 6.8 Self-Learning When the Number of Regions Is Un-known: I; 6.9 Self-Learning When the Number of Regions Is Un- known: II; 6.10 Discussion; 6.11 Conclusion; Comments; References; Chapter VII. Learning Models; 7.1 Introduction; 7.2 Description of the System; 7.3 Structure of the Model; 7.4 Goal of Learning
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7.5 Algorithms of Learning
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English
Additional Edition:
ISBN 0-12-702060-8
Language:
English
