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OCoLC |
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20231117044438.0 |
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m o d |
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cr un||||a|a|| |
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100809s1991 caua ob 101 0 eng d |
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|a OCLCE
|b eng
|e pn
|c OCLCE
|d OCLCQ
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| 019 |
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|a 897646141
|a 961590433
|a 1156371687
|a 1162070318
|a 1175724204
|a 1202483866
|a 1202540849
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| 020 |
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|a 9781483214481
|q (electronic bk.)
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|a 1483214486
|q (electronic bk.)
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|a 1322469962
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|a 9781322469966
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|z 1558601562
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|z 9781558601567
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|a (OCoLC)654840197
|z (OCoLC)897646141
|z (OCoLC)961590433
|z (OCoLC)1156371687
|z (OCoLC)1162070318
|z (OCoLC)1175724204
|z (OCoLC)1202483866
|z (OCoLC)1202540849
|
| 050 |
|
4 |
|a QA76.5
|b .C61938 1991
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| 072 |
|
7 |
|a COM
|x 000000
|2 bisacsh
|
| 082 |
0 |
4 |
|a 006.3
|2 20
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| 245 |
0 |
0 |
|a Connectionist models :
|b proceedings of the 1990 summer school /
|c edited by David S. Touretzky [and others].
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| 260 |
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|a San Mateo, Calif. :
|b M. Kaufmann Publishers,
|c �1991.
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| 300 |
|
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|a 1 online resource (xi, 404 pages) :
|b illustrations
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| 336 |
|
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 504 |
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|a Includes bibliographical references and index.
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| 588 |
0 |
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|a Print version record.
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| 505 |
0 |
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|a Front Cover; Connectionist Models; Copyright Page; Table of Contents; Foreword; Participants in the 1990 Connectionist Models Summer School; List Of Accepted Students; Part I: Mean Field, Boltzmann, and Hopfield Networks; Chapter 1. Deterministic Boltzmann Learning in Networks with Asymmetric Connectivity; Abstract; 1 INTRODUCTION; 2 DETERMINISTIC BOLTZMANN LEARNING IN SYMMETRIC NETWORKS; 3 ASYMMETRIC NETWORKS; 4 SIMULATION RESULTS; 5 DISCUSSION; Acknowledgement; References; APPENDIX; Chapter 2. Contrastive Hebbian Learning in the Continuous Hopfield Model; Abstract; 1 INTRODUCTION.
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| 505 |
8 |
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|a 2 STABILITY OF ACTIVATIONS3 CONTRASTIVE LEARNING; 4 DISCUSSION; 5 APPENDIX; Acknowledgements; References; Chapter 3. Mean field networks that learn to discriminate temporally distorted strings; Abstract; INTRODUCTION; PREVIOUS APPROACHES USING NEURAL NETS; THE LEARNING PROCEDURE FOR THE MEAN FIELD MODULES; THE TASK USED IN THE SIMULATIONS; RESULTS AND DISCUSSION; Acknowledgements; References; Chapter 4. Energy Minimization and the Satisfiability of Propositional Logic; Abstract; 1 Introduction; 2 Satisfiability and models of propositional formulas; 3 Equivalence between WFFs.
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|a 4 Conversion of a WFF into Conjunction of Triples Form (CTF)5 Energy functions; 6 The equivalence between high order models and low order models; 7 Describing WFFs by energy functions; 8 The penalty function; 9 Mapping from a satisfiability problem to a minimization problem and vice versa; 10 Summary, applications and conclusions; Acknowledgments; References; Part II: Reinforcement Learning; Chapter 5. On the Computational Economics of Reinforcement Learning; Abstract; 1 INTRODUCTION; 2 INDIRECT AND DIRECT ADAPTIVE CONTROL; 3 MARKOV DECISION PROBLEMS.
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| 505 |
8 |
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|a 4 INDIRECT AND DIRECT LEARNING FOR MARKOV DECISION PROBLEMS5 AN INDIRECT ALGORITHM; 6 Q-LEARNING; 7 SIMULATION RESULTS; 8 DISCUSSION; 9 CONCLUSION; Acknowledgements; References; Chapter 6. Reinforcement Comparison; Abstract; 1 INTRODUCTION; 2 THEORY; 3 RESULTS; 4 CONCLUSIONS; Acknowledgements; References; Chapter 7. Learning Algorithms for Networks with Internal and External Feedback; Abstract; 1 Terminology; 2 The Neural Bucket Brigade Algorithm; 3 A Reinforcement Comparison Algorithm for Continually Running Fully Recurrent Probabilistic Networks.
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| 505 |
8 |
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|a 4 Two Interacting Fully Recurrent Self-Supervised Learning Networks for Reinforcement Learning5 An Example for Learning Dynamic Selective Attention: Adaptive Focus Trajectories for Attentive Vision; 6 An Adaptive Subgoal Generator for Planning Action Sequences; References; Part III: Genetic Learning; Chapter 8. Exploring Adaptive Agency I: Theory and Methods for Simulating the Evolution of Learning; Abstract; 1 INTRODUCTION; 2 NATURAL SELECTION AND THE EVOLUTION OF SUBSIDIARY ADAPTIVE PROCESSES; 3 A BRIEF HISTORY OF LEARNING THEORY IN (COMPARATIVE) PSYCHOLOGY.
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| 546 |
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|a English.
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| 650 |
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0 |
|a Connection machines
|v Congresses.
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| 650 |
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0 |
|a Neural networks (Computer science)
|v Congresses.
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| 650 |
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6 |
|a Connection Machine
|0 (CaQQLa)201-0234422
|v Congr�es.
|0 (CaQQLa)201-0378219
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| 650 |
|
6 |
|a R�eseaux neuronaux (Informatique)
|0 (CaQQLa)201-0209597
|v Congr�es.
|0 (CaQQLa)201-0378219
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| 650 |
|
7 |
|a COMPUTERS
|x General.
|2 bisacsh
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| 650 |
|
7 |
|a Connection machines.
|2 fast
|0 (OCoLC)fst00875334
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| 650 |
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7 |
|a Neural networks (Computer science)
|2 fast
|0 (OCoLC)fst01036260
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| 650 |
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7 |
|a Neuronales Netz
|2 gnd
|0 (DE-588)4226127-2
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| 650 |
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7 |
|a Konnektionismus
|2 gnd
|0 (DE-588)4265446-4
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| 650 |
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7 |
|a Kongress
|2 gnd
|0 (DE-588)4130470-6
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| 655 |
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2 |
|a Congress
|0 (DNLM)D016423
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| 655 |
|
7 |
|a Conference papers and proceedings.
|2 fast
|0 (OCoLC)fst01423772
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| 655 |
|
7 |
|a Conference papers and proceedings.
|2 lcgft
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| 655 |
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7 |
|a Actes de congr�es.
|2 rvmgf
|0 (CaQQLa)RVMGF-000001049
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| 700 |
1 |
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|a Touretzky, David S.
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| 711 |
2 |
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|a Connectionist Models Summer School
|d (1990 :
|c University of California, San Diego)
|
| 776 |
0 |
8 |
|i Print version:
|t Connectionist models.
|d San Mateo, Calif. : M. Kaufmann Publishers, �1991
|w (DLC) 90021144
|w (OCoLC)22625008
|
| 856 |
4 |
0 |
|u https://sciencedirect.uam.elogim.com/science/book/9781483214481
|z Texto completo
|
