Networks of Learning Automata [E-Book] : Techniques for Online Stochastic Optimization / by M. A. L. Thathachar, P. S. Sastry.
Networks of Learning Automata: Techniques for Online Stochastic Optimization is a comprehensive account of learning automata models with emphasis on multiautomata systems. It considers synthesis of complex learning structures from simple building blocks and uses stochastic algorithms for refining pr...
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Full text |
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Personal Name(s): | Thathachar, M. A. L., author |
Sastry, P. S., author | |
Imprint: |
Boston, MA :
Springer,
2004
|
Physical Description: |
XV, 268 p. online resource. |
Note: |
englisch |
ISBN: |
9781441990525 |
DOI: |
10.1007/978-1-4419-9052-5 |
Subject (LOC): |
- 1. Introduction
- 1.1 Machine Intelligence and Learning
- 1.2 Learning Automata
- 1.3 The Finite Action Learning Automaton (FALA)
- 1.4 Some Classical Learning Algorithms
- 1.5 The Discretized Probability FALA
- 1.6 The Continuous Action Learning Automaton (CALA)
- 1.7 The Generalized Learning Automaton (GLA)
- 1.8 The Parameterized Learning Automaton (PLA)
- 1.9 Multiautomata Systems
- 1.10 Supplementary Remarks
- 2. Games of Learning Automata
- 2.1 Introduction
- 2.2 A Multiple Payoff Stochastic Game of Automata
- 2.3 Analysis of the Automata Game Algorithm
- 2.4 Game with Common Payoff
- 2.5 Games of FALA
- 2.6 Common Payoff Games of CALA
- 2.7 Applications
- 2.8 Discussion
- 2.9 Supplementary Remarks
- 3. Feedforward Networks
- 3.1 Introduction
- 3.2 Networks of FALA
- 3.3 The Learning Model
- 3.4 The Learning Algorithm
- 3.5 Analysis
- 3.6 Extensions
- 3.7 Convergence to the Global Maximum
- 3.8 Networks of GLA
- 3.9 Discussion
- 3.10 Supplementary Remarks
- 4. Learning Automata for Pattern Classification
- 4.1 Introduction
- 4.2 Pattern Recognition
- 4.3 Common Payoff Game of Automata for PR
- 4.4 Automata Network for Pattern Recognition
- 4.5 Decision Tree Classifiers
- 4.6 Discussion
- 4.7 Supplementary Remarks
- 5. Parallel Operation of Learning Automata
- 5.1 Introduction
- 5.2 Parallel Operation of FALA
- 5.3 Parallel Operation of CALA
- 5.4 Parallel Pursuit Algorithm
- 5.5 General Procedure
- 5.6 Parallel Operation of Games of FALA
- 5.7 Parallel Operation of Networks of FALA
- 5.8 Discussion
- 5.9 Supplementary Remarks
- 6. Some Recent Applications
- 6.1 Introduction
- 6.2 Supervised Learning of Perceptual Organization in Computer Vision
- 6.3 Distributed Control of Broadcast Communication Networks
- 6.4O ther Applications
- 6.5 Discussion
- Epilogue
- Appendices
- A The ODE Approach to Analysis of Learning Algorithms
- A.I Introduction
- A.2 Derivation of the ODE Approximation
- A.2.1 Assumptions
- A.2.2 Analysis
- A.3 Approximating ODEs for Some Automata Algorithms
- A.3.2 The CALA Algorithm
- A.3.3 Automata Team Algorithms
- A.4 Relaxing the Assumptions
- B Proofs of Convergence for Pursuit Algorithm
- B.1 Proof of Theorem 1.1
- B.2 Proof of Theorem 5.7
- C Weak Convergence and SDE Approximations
- C.I Introduction
- C.2 Weak Convergence
- C.3 Convergence to SDE
- C.3.1 Application to Global Algorithms
- C.4 Convergence to ODE
- References.