Foundations of the Theory of Learning Systems

Name: Foundations of the Theory of Learning Systems
Brand: Academic Press
Price: 50.95 EUR
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Tsypkin(Autor*in)

Tsypkin(Herausgeber*in)

Academic Press

Erschienen am 14. Mai 2014

221 Seiten

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978-0-08-095610-7 (ISBN)

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Inhalt

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
2.8 Special Cases
2.9 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
3.10 Continuous and Hybrid Algorithms of Optimal Learning
3.11 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
4.11 General Decision Rule
4.12 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
Chapter VI. Self-Learning Systems of Classification
6.1 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
7.5 Algorithms of Learning
7.6 Linear Learning Model
7.7 Optimal Learning Linear Model
7.8 Nonlinear Learning Model: I
7.9 Nonlinear Learning Model: II
7.10 Discussion
7.1 1 Influence of Noise
7.12 Removing the Influence of Noise
7.13 Conclusion
Comments
References
Chapter VIII. Learning Filters
8.1 Introduction
8.2 Statement of the Problem
8.3 Structure of the Filter
8.4 Optimal Wiener Filter
8.5 Learning Wiener Filter
8.6 Learning Wiener Filter with Known A Priori Information about Noise
8.7 Learning Wiener Filter with Known A Priori Information about the Signal
8.8 A Generalization
8.9 Optimal Learning Wiener Filters
8.10 Learning Filters of Another Type
8.11 Optimal Learning Filter
8.12 Conclusion
Comments
References
Chapter IX. Examples of Learning Systems
9.1 Introduction
9.2 Perceptron
9.3 Adaline
9.4 Learning Receiver: I
9.5 Learning Receiver: II
9.6 Self-Learning Classifier
9.7 Learning Filters
9.8 Learning Antenna System
9.9 Learning Communication System
9.10 Learning Coding Device
9.11 Self-Learning Sampler
9.12 Learning Control System
9.13 Learning Diagnostic Systems
9.14 Establishment of Parametric Sequences
Comments
References
Epilogue
Author Index
Subject Index

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