Evolution, learning, and cognition /:
This review volume represents the first attempt to provide a comprehensive overview of this exciting and rapidly evolving development. The book comprises specially commissioned articles by leading researchers in the areas of neural networks and connectionist systems, classifier systems, adaptive net...
Gespeichert in:
| Weitere Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Singapore ; Teaneck, N.J., USA :
World Scientific,
©1988.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | This review volume represents the first attempt to provide a comprehensive overview of this exciting and rapidly evolving development. The book comprises specially commissioned articles by leading researchers in the areas of neural networks and connectionist systems, classifier systems, adaptive network systems, genetic algorithm, cellular automata, artificial immune systems, evolutionary genetics, cognitive science, optical computing, combinatorial optimization, and cybernetics. |
| Beschreibung: | 1 online resource (x, 411 pages) : illustrations |
| Bibliographie: | Includes bibliographical references. |
| ISBN: | 9789814434102 9814434108 |
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| 505 | 0 | |a PREFACE; CONTENTS; Part One MATHEMATICAL THEORY; Connectionist Learning Through Gradient Following; INTRODUCTION; CONNECTIONIST SYSTEMS; LEARNING; Supervised Learning vs. Associative Reinforcement Learning; FORMAL ASSUMPTIONS AND NOTATION; BACK-PROPAGATION ALGORITHM FOR SUPERVISED LEARNING; Extended Back-Propagation; REINFORCE ALGORITHMS FOR ASSOCIATIVE REINFORCEMENT LEARNING; Extended REINFORCE Algorithms; DISCUSSION; SUMMARY; REFERENCES; Efficient Stochastic Gradient Learning Algorithm for Neural Network; 1 Introduction; 2 Learning as Stochastic Gradient Descents. | |
| 505 | 8 | |a 3 Convergence Theorems for First Order Schemes4 Convergence of the Second Order Schemes; 5 Discussion; References; INFORMATION STORAGE IN FULLY CONNECTED NETWORKS; 1 INTRODUCTION; 1.1 Neural Networks; 1.2 Organisation; 1.3 Notation; 2 THE MODEL OF McCULLOCH-PITTS; 2.1 State-Theoretic Description; 2.2 Associative Memory; 3 THE OUTER-PRODUCT ALGORITHM; 3.1 The Model; 3.2 Storage Capacity; 4 SPECTRAL ALGORITHMS; 4.1 Outer-Products Revisited; 4.2 Constructive Spectral Approaches; 4.3 Basins of Attraction; 4.4 Choice of Eigenvalues; 5 COMPUTER SIMULATIONS; 6 DISCUSSION; A PROPOSITIONS. | |
| 505 | 8 | |a B OUTER-PRODUCT THEOREMSC PROOFS OF SPECTRAL THEOREMS; References; NEURONIC EQUATIONS AND THEIR SOLUTIONS; 1. Introduction; 1.1. Reminiscing; 1.2. The 1961 Model; 1.3. Notation; 2. Linear Separable NE; 2.1. Neuronic Equations; 2.2. Polygonal Inequalities; 2.3. Computation of the n-expansion of arbitrary l.s. functions; 2.4. Continuous versus discontinuous behaviour: transitions; 3. General Boolean NE; 3.1. Linearization in tensor space; 3.2. Next-state matrix; 3.3. Normal modes, attractors; 3.4. Synthesis of nets: the inverse problem; 3.5. Separable versus Boolean nets. | |
| 505 | 8 | |a Connections with spin formalismReferences; The Dynamics of Searches Directed by Genetic Algorithms; The Hyperplane Transformation.; The Genetic Algorithm as a Hyperplane-Directed Search Procedure; (1) Description of the genetic algorithm; (2) Effects of the S's on the search generated by a genetic algorithm.; (3) An Example.; References.; PROBABILISTIC NEURAL NETWORKS; 1. INTRODUCTION; 2. MODELING THE NOISY NEURON; 2.1. Empirical Properties of Neuron and Synapse; 22. Model of Shaw and Vasudevan; 2.3. Model of Little; 2.4. Model of Taylor. | |
| 505 | 8 | |a 3. NONEQUILIBRIUM STATISTICAL MECHANICS OF LINEAR MODELS3.1. Statistical Law of Motion -- Markov Chain and Master Equation; 3.2. Entropy Production in the Neural; 3.3. Macroscopic Forces and Fluxes; 3.4. Conditions for Thermodynamic Equilibrium; 3.5. Implications for Memory Storage: How Dire?; 4. DYNAMICAL PROPERTIES OF NONLINEAR MODELS; 4.1. Views of Statistical Dynamics; 4.2. Multineuron Interactions, Revisited; 4.3. Cognitive Aspects of the Taylor Model; 4.4. Noisy RAMS and Noisy Nets; 5. THE END OF THE BEGINNING; ACKNOWLEDGMENTS; APPENDIX. TRANSITION PROBABILITIES IN 2-NEURON NETWORKS. | |
| 520 | |a This review volume represents the first attempt to provide a comprehensive overview of this exciting and rapidly evolving development. The book comprises specially commissioned articles by leading researchers in the areas of neural networks and connectionist systems, classifier systems, adaptive network systems, genetic algorithm, cellular automata, artificial immune systems, evolutionary genetics, cognitive science, optical computing, combinatorial optimization, and cybernetics. | ||
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| contents | PREFACE; CONTENTS; Part One MATHEMATICAL THEORY; Connectionist Learning Through Gradient Following; INTRODUCTION; CONNECTIONIST SYSTEMS; LEARNING; Supervised Learning vs. Associative Reinforcement Learning; FORMAL ASSUMPTIONS AND NOTATION; BACK-PROPAGATION ALGORITHM FOR SUPERVISED LEARNING; Extended Back-Propagation; REINFORCE ALGORITHMS FOR ASSOCIATIVE REINFORCEMENT LEARNING; Extended REINFORCE Algorithms; DISCUSSION; SUMMARY; REFERENCES; Efficient Stochastic Gradient Learning Algorithm for Neural Network; 1 Introduction; 2 Learning as Stochastic Gradient Descents. 3 Convergence Theorems for First Order Schemes4 Convergence of the Second Order Schemes; 5 Discussion; References; INFORMATION STORAGE IN FULLY CONNECTED NETWORKS; 1 INTRODUCTION; 1.1 Neural Networks; 1.2 Organisation; 1.3 Notation; 2 THE MODEL OF McCULLOCH-PITTS; 2.1 State-Theoretic Description; 2.2 Associative Memory; 3 THE OUTER-PRODUCT ALGORITHM; 3.1 The Model; 3.2 Storage Capacity; 4 SPECTRAL ALGORITHMS; 4.1 Outer-Products Revisited; 4.2 Constructive Spectral Approaches; 4.3 Basins of Attraction; 4.4 Choice of Eigenvalues; 5 COMPUTER SIMULATIONS; 6 DISCUSSION; A PROPOSITIONS. B OUTER-PRODUCT THEOREMSC PROOFS OF SPECTRAL THEOREMS; References; NEURONIC EQUATIONS AND THEIR SOLUTIONS; 1. Introduction; 1.1. Reminiscing; 1.2. The 1961 Model; 1.3. Notation; 2. Linear Separable NE; 2.1. Neuronic Equations; 2.2. Polygonal Inequalities; 2.3. Computation of the n-expansion of arbitrary l.s. functions; 2.4. Continuous versus discontinuous behaviour: transitions; 3. General Boolean NE; 3.1. Linearization in tensor space; 3.2. Next-state matrix; 3.3. Normal modes, attractors; 3.4. Synthesis of nets: the inverse problem; 3.5. Separable versus Boolean nets. Connections with spin formalismReferences; The Dynamics of Searches Directed by Genetic Algorithms; The Hyperplane Transformation.; The Genetic Algorithm as a Hyperplane-Directed Search Procedure; (1) Description of the genetic algorithm; (2) Effects of the S's on the search generated by a genetic algorithm.; (3) An Example.; References.; PROBABILISTIC NEURAL NETWORKS; 1. INTRODUCTION; 2. MODELING THE NOISY NEURON; 2.1. Empirical Properties of Neuron and Synapse; 22. Model of Shaw and Vasudevan; 2.3. Model of Little; 2.4. Model of Taylor. 3. NONEQUILIBRIUM STATISTICAL MECHANICS OF LINEAR MODELS3.1. Statistical Law of Motion -- Markov Chain and Master Equation; 3.2. Entropy Production in the Neural; 3.3. Macroscopic Forces and Fluxes; 3.4. Conditions for Thermodynamic Equilibrium; 3.5. Implications for Memory Storage: How Dire?; 4. DYNAMICAL PROPERTIES OF NONLINEAR MODELS; 4.1. Views of Statistical Dynamics; 4.2. Multineuron Interactions, Revisited; 4.3. Cognitive Aspects of the Taylor Model; 4.4. Noisy RAMS and Noisy Nets; 5. THE END OF THE BEGINNING; ACKNOWLEDGMENTS; APPENDIX. TRANSITION PROBABILITIES IN 2-NEURON NETWORKS. |
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| id | ZDB-4-EBU-ocn842936583 |
| illustrated | Illustrated |
| indexdate | 2024-11-26T14:49:10Z |
| institution | BVB |
| isbn | 9789814434102 9814434108 |
| language | English |
| oclc_num | 842936583 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (x, 411 pages) : illustrations |
| psigel | ZDB-4-EBU |
| publishDate | 1988 |
| publishDateSearch | 1988 |
| publishDateSort | 1988 |
| publisher | World Scientific, |
| record_format | marc |
| spelling | Evolution, learning, and cognition / editor, Y.C. Lee. Singapore ; Teaneck, N.J., USA : World Scientific, ©1988. 1 online resource (x, 411 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Includes bibliographical references. Print version record. PREFACE; CONTENTS; Part One MATHEMATICAL THEORY; Connectionist Learning Through Gradient Following; INTRODUCTION; CONNECTIONIST SYSTEMS; LEARNING; Supervised Learning vs. Associative Reinforcement Learning; FORMAL ASSUMPTIONS AND NOTATION; BACK-PROPAGATION ALGORITHM FOR SUPERVISED LEARNING; Extended Back-Propagation; REINFORCE ALGORITHMS FOR ASSOCIATIVE REINFORCEMENT LEARNING; Extended REINFORCE Algorithms; DISCUSSION; SUMMARY; REFERENCES; Efficient Stochastic Gradient Learning Algorithm for Neural Network; 1 Introduction; 2 Learning as Stochastic Gradient Descents. 3 Convergence Theorems for First Order Schemes4 Convergence of the Second Order Schemes; 5 Discussion; References; INFORMATION STORAGE IN FULLY CONNECTED NETWORKS; 1 INTRODUCTION; 1.1 Neural Networks; 1.2 Organisation; 1.3 Notation; 2 THE MODEL OF McCULLOCH-PITTS; 2.1 State-Theoretic Description; 2.2 Associative Memory; 3 THE OUTER-PRODUCT ALGORITHM; 3.1 The Model; 3.2 Storage Capacity; 4 SPECTRAL ALGORITHMS; 4.1 Outer-Products Revisited; 4.2 Constructive Spectral Approaches; 4.3 Basins of Attraction; 4.4 Choice of Eigenvalues; 5 COMPUTER SIMULATIONS; 6 DISCUSSION; A PROPOSITIONS. B OUTER-PRODUCT THEOREMSC PROOFS OF SPECTRAL THEOREMS; References; NEURONIC EQUATIONS AND THEIR SOLUTIONS; 1. Introduction; 1.1. Reminiscing; 1.2. The 1961 Model; 1.3. Notation; 2. Linear Separable NE; 2.1. Neuronic Equations; 2.2. Polygonal Inequalities; 2.3. Computation of the n-expansion of arbitrary l.s. functions; 2.4. Continuous versus discontinuous behaviour: transitions; 3. General Boolean NE; 3.1. Linearization in tensor space; 3.2. Next-state matrix; 3.3. Normal modes, attractors; 3.4. Synthesis of nets: the inverse problem; 3.5. Separable versus Boolean nets. Connections with spin formalismReferences; The Dynamics of Searches Directed by Genetic Algorithms; The Hyperplane Transformation.; The Genetic Algorithm as a Hyperplane-Directed Search Procedure; (1) Description of the genetic algorithm; (2) Effects of the S's on the search generated by a genetic algorithm.; (3) An Example.; References.; PROBABILISTIC NEURAL NETWORKS; 1. INTRODUCTION; 2. MODELING THE NOISY NEURON; 2.1. Empirical Properties of Neuron and Synapse; 22. Model of Shaw and Vasudevan; 2.3. Model of Little; 2.4. Model of Taylor. 3. NONEQUILIBRIUM STATISTICAL MECHANICS OF LINEAR MODELS3.1. Statistical Law of Motion -- Markov Chain and Master Equation; 3.2. Entropy Production in the Neural; 3.3. Macroscopic Forces and Fluxes; 3.4. Conditions for Thermodynamic Equilibrium; 3.5. Implications for Memory Storage: How Dire?; 4. DYNAMICAL PROPERTIES OF NONLINEAR MODELS; 4.1. Views of Statistical Dynamics; 4.2. Multineuron Interactions, Revisited; 4.3. Cognitive Aspects of the Taylor Model; 4.4. Noisy RAMS and Noisy Nets; 5. THE END OF THE BEGINNING; ACKNOWLEDGMENTS; APPENDIX. TRANSITION PROBABILITIES IN 2-NEURON NETWORKS. This review volume represents the first attempt to provide a comprehensive overview of this exciting and rapidly evolving development. The book comprises specially commissioned articles by leading researchers in the areas of neural networks and connectionist systems, classifier systems, adaptive network systems, genetic algorithm, cellular automata, artificial immune systems, evolutionary genetics, cognitive science, optical computing, combinatorial optimization, and cybernetics. Neural computers. http://id.loc.gov/authorities/subjects/sh87008041 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Cognitive science. http://id.loc.gov/authorities/subjects/sh88006179 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Ordinateurs neuronaux. Intelligence artificielle. Sciences cognitives. artificial intelligence. aat COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Artificial intelligence fast Cognitive science fast Neural computers fast Künstliche Intelligenz gnd Kognitives Lernen gnd http://d-nb.info/gnd/4164479-7 Aufsatzsammlung gnd Neurocomputer gnd Aprendizagem (psicologia) larpcal Lee, Y. C. (Yee Chun) https://id.oclc.org/worldcat/entity/E39PCjFXcVwbcDmQcyhY8x8h6q http://id.loc.gov/authorities/names/n87852242 Print version: Evolution, learning, and cognition. Singapore ; Teaneck, N.J., USA : World Scientific, ©1988 9971505290 (DLC) 88033806 (OCoLC)18833237 FWS01 ZDB-4-EBU FWS_PDA_EBU https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=575367 Volltext |
| spellingShingle | Evolution, learning, and cognition / PREFACE; CONTENTS; Part One MATHEMATICAL THEORY; Connectionist Learning Through Gradient Following; INTRODUCTION; CONNECTIONIST SYSTEMS; LEARNING; Supervised Learning vs. Associative Reinforcement Learning; FORMAL ASSUMPTIONS AND NOTATION; BACK-PROPAGATION ALGORITHM FOR SUPERVISED LEARNING; Extended Back-Propagation; REINFORCE ALGORITHMS FOR ASSOCIATIVE REINFORCEMENT LEARNING; Extended REINFORCE Algorithms; DISCUSSION; SUMMARY; REFERENCES; Efficient Stochastic Gradient Learning Algorithm for Neural Network; 1 Introduction; 2 Learning as Stochastic Gradient Descents. 3 Convergence Theorems for First Order Schemes4 Convergence of the Second Order Schemes; 5 Discussion; References; INFORMATION STORAGE IN FULLY CONNECTED NETWORKS; 1 INTRODUCTION; 1.1 Neural Networks; 1.2 Organisation; 1.3 Notation; 2 THE MODEL OF McCULLOCH-PITTS; 2.1 State-Theoretic Description; 2.2 Associative Memory; 3 THE OUTER-PRODUCT ALGORITHM; 3.1 The Model; 3.2 Storage Capacity; 4 SPECTRAL ALGORITHMS; 4.1 Outer-Products Revisited; 4.2 Constructive Spectral Approaches; 4.3 Basins of Attraction; 4.4 Choice of Eigenvalues; 5 COMPUTER SIMULATIONS; 6 DISCUSSION; A PROPOSITIONS. B OUTER-PRODUCT THEOREMSC PROOFS OF SPECTRAL THEOREMS; References; NEURONIC EQUATIONS AND THEIR SOLUTIONS; 1. Introduction; 1.1. Reminiscing; 1.2. The 1961 Model; 1.3. Notation; 2. Linear Separable NE; 2.1. Neuronic Equations; 2.2. Polygonal Inequalities; 2.3. Computation of the n-expansion of arbitrary l.s. functions; 2.4. Continuous versus discontinuous behaviour: transitions; 3. General Boolean NE; 3.1. Linearization in tensor space; 3.2. Next-state matrix; 3.3. Normal modes, attractors; 3.4. Synthesis of nets: the inverse problem; 3.5. Separable versus Boolean nets. Connections with spin formalismReferences; The Dynamics of Searches Directed by Genetic Algorithms; The Hyperplane Transformation.; The Genetic Algorithm as a Hyperplane-Directed Search Procedure; (1) Description of the genetic algorithm; (2) Effects of the S's on the search generated by a genetic algorithm.; (3) An Example.; References.; PROBABILISTIC NEURAL NETWORKS; 1. INTRODUCTION; 2. MODELING THE NOISY NEURON; 2.1. Empirical Properties of Neuron and Synapse; 22. Model of Shaw and Vasudevan; 2.3. Model of Little; 2.4. Model of Taylor. 3. NONEQUILIBRIUM STATISTICAL MECHANICS OF LINEAR MODELS3.1. Statistical Law of Motion -- Markov Chain and Master Equation; 3.2. Entropy Production in the Neural; 3.3. Macroscopic Forces and Fluxes; 3.4. Conditions for Thermodynamic Equilibrium; 3.5. Implications for Memory Storage: How Dire?; 4. DYNAMICAL PROPERTIES OF NONLINEAR MODELS; 4.1. Views of Statistical Dynamics; 4.2. Multineuron Interactions, Revisited; 4.3. Cognitive Aspects of the Taylor Model; 4.4. Noisy RAMS and Noisy Nets; 5. THE END OF THE BEGINNING; ACKNOWLEDGMENTS; APPENDIX. TRANSITION PROBABILITIES IN 2-NEURON NETWORKS. Neural computers. http://id.loc.gov/authorities/subjects/sh87008041 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Cognitive science. http://id.loc.gov/authorities/subjects/sh88006179 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Ordinateurs neuronaux. Intelligence artificielle. Sciences cognitives. artificial intelligence. aat COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Artificial intelligence fast Cognitive science fast Neural computers fast Künstliche Intelligenz gnd Kognitives Lernen gnd http://d-nb.info/gnd/4164479-7 Aufsatzsammlung gnd Neurocomputer gnd Aprendizagem (psicologia) larpcal |
| subject_GND | http://id.loc.gov/authorities/subjects/sh87008041 http://id.loc.gov/authorities/subjects/sh85008180 http://id.loc.gov/authorities/subjects/sh88006179 https://id.nlm.nih.gov/mesh/D001185 http://d-nb.info/gnd/4164479-7 |
| title | Evolution, learning, and cognition / |
| title_auth | Evolution, learning, and cognition / |
| title_exact_search | Evolution, learning, and cognition / |
| title_full | Evolution, learning, and cognition / editor, Y.C. Lee. |
| title_fullStr | Evolution, learning, and cognition / editor, Y.C. Lee. |
| title_full_unstemmed | Evolution, learning, and cognition / editor, Y.C. Lee. |
| title_short | Evolution, learning, and cognition / |
| title_sort | evolution learning and cognition |
| topic | Neural computers. http://id.loc.gov/authorities/subjects/sh87008041 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Cognitive science. http://id.loc.gov/authorities/subjects/sh88006179 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Ordinateurs neuronaux. Intelligence artificielle. Sciences cognitives. artificial intelligence. aat COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Artificial intelligence fast Cognitive science fast Neural computers fast Künstliche Intelligenz gnd Kognitives Lernen gnd http://d-nb.info/gnd/4164479-7 Aufsatzsammlung gnd Neurocomputer gnd Aprendizagem (psicologia) larpcal |
| topic_facet | Neural computers. Artificial intelligence. Cognitive science. Artificial Intelligence Ordinateurs neuronaux. Intelligence artificielle. Sciences cognitives. artificial intelligence. COMPUTERS Enterprise Applications Business Intelligence Tools. COMPUTERS Intelligence (AI) & Semantics. Artificial intelligence Cognitive science Neural computers Künstliche Intelligenz Kognitives Lernen Aufsatzsammlung Neurocomputer Aprendizagem (psicologia) |
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