Neural Networks and Intellect:Using Model Based Concepts

Neural Networks and Intellect:Using Model Based Concepts
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售价：

发货周期：预计5-7周发货

作者

Perlovsky, Leonid

出版社

Oxford University Press

出版时间

2000年11月23日

装帧

精装

ＩＳＢＮ

9780195111620

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页码

496

开本

234x186mm

语种

英文

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This is a textbook for courses commonly called neural networks in departments of computer and information science. This unique neural network book will describe novel architectures and learning mechanisms of model-based neural networks that utilize and intriguing concept of an internal "world" model. This concept combines a prior knowledge of models with adaptive learning and addresses the most perplexing problems in the fields of neural networks: fast learning and robust generalization. The author provides an overview of neural networks and artificial intelligence fields, relating hundreds of seemingly disparate techniques to several basic mathematical concepts. He then analyzes fundamental computational concepts of major neural network paradigms, and relates them to concepts of mind in philosophy, pschology, and linguistics. Relationships of these mathematical concepts to the concepts of philosophy will help students and researchers determine the directions of future research. This book can also be used as a supplementary text in a graduate course on Neural Networks.

Part I. Overview. 2300 years of philosophy; 100 years of mathematical logic and 50 years of computational intelligence; 1: Introduction. Concepts of Intelligence; 1.1: Concepts of Intelligence in Mathematics, Psychology, and Philosophy; 1.2: Probability, Hypothesis Choice, Pattern Recognition, and Complexity; 1.3: Prediction, Tracking, and Dynamical Models; 1.4: Preview: Intelligence, Internal Model, Symbol, Emotions and Consciousness; Notes; Bibliographical Notes; Problems; 2: Mathematical Concepts of Mind; 2.1: Complexity, Aristotle, and Fuzzy Logic; 2.2: Nearest Neighbors and Degenerate Geometries; 2.3: Gradient Learning, Back Propagation and Feedforward Neural Networks; 2.4: Rule-Based Artificial Intelligence; 2.5: Concept of Internal Model; 2.6: Abductive Reasoning; 2.7: Statistical Learning Theory and Support Vector Machines; 2.8: AI Debates Past and Future; 2.9: Societ of Mind; 2.10: Sensor Fusion and JDL Model; 2.11: Hierarchical Organization; 2.12: Semiotics; 2.13: Evolutionary Computation, Genetic Algorithms, and CAS; 2.14: Neural Field Theories; 2.15: Intelligence, Learning, and Computability; Problems; Bibliographical Notes; Notes; 3: Mathematical vs. Metaphysical Concepts of Mind; 3.1: Prolegomenon. Plato, Antisthenes, and Artifical Intelligence; 3.2: Learning from Aristotle to Maimonides; 3.3: Heresy of Occam and Scientific Method; 3.4: Mathematics vs. Physics; 3.5: Kant: Pure Spirit and Psychology; 3.6: Freud vs. Jung. Psychology of Philosophy; 3.7: Wither We Go From Here?; Notes; Bibliographical Notes; Part II. Modeling Field Theory. New mathmatical theory of intelligence with examples of engineering applications; 4: Modeling Field Theory and Model-Based Neural Networks; 4.1: Internal Models, Uncertainties, and Similarities; 4.2: Modeling Field Theory Dynamics; 4.3: Bayesian MFT; 4.4: Shannon-Einsteinian MFT; 4.5: Modeling Field Theory Neural Architecture; 4.6: Convergence; 4.7: Learning of Structures and AIC; 4.8: Instinct of World Modeling: Knowledge Instinct; 4.9: Summary; 5: Maximum Likelihood Adaptive Neural System (MLANS) for Grouping and Recognition; 5.1: Grouping, Recognition and Models; 5.2: Gaussian Mixture Model. Unsupervised Learning; 5.3: Combined Unsupervised and Interactive Learning; 5.4: Structure Estimation; 5.5: Wishart and Rician Mixture Models for Radar Image Classification; 5.6: Convergence; 5.7: MLANS, Physics, Biology, and Other Neural Networks; Notes; Bibliographical Notes; Problems; 6: Einsteinian Neural Network (ENN) for Signal and Image Processing; 6.1: Images, Signals, and Spectra; 6.2: Spectral Models; 6.3: Neural Dynamics of ENN; 6.4: Applications to Acoustic Transient Signals and Speech Recognition; 6.5: Applications to Electromagnetic Wave Propagation in Ionosphere; 6.6: Summary; Appendix; Notes; Bibliograhical Notes; Problems; 7: Prediction, Association, Tracking, and Information Fusion; 7.1: Prediction, Association, and Non-linear Regression; 7.2: Association and Tracking Using Bayesian MFT; 7.3: Association and Tracking Using Shannon-Einsteinian MFT (SE-CAT); 7.4: Sensor Fusion MFT; 7.5: Attention; Notes; Bibliographical Notes; Problems; 8: Quantum Modeling Field Theory (QMFT); 8.1: Quantum Computing and Quantum Physics Notations; 8.2: Gibbs Quantum Modeling Field System; 8.3: Hamiltonian Quantum Modeling Field System; Bibliographical Notes; Problems; 9: Fundamental Limitations on Learning; 9.1: The Cramer-Rao Bound (CRB) on Speed of Learning; 9.2: Overlap Between Classes; 9.3: CRB for MLANS; 9.4: CRB for Concurrent Association and Tracking (CAT); 9.5: Summary. Bounds for Intellect and Evolution?; Appendix. CRB Rule-of-Thumb for CAT; Notes; Bibliographical Notes; Problems; 10: Intelligent Systems Organization, Kant vs. MFT; 10.1: Kant, MFT and Intelligent Systems; 10.2: Emotional Machines (Toward Mathematics of Beauty); 10.3: Learning: Genetic Algorithms, MFT and Semiosis; Notes; Bibliographical Notes; Problems; Part III. Futuristic Directions. Fun Stuff. Mind: Physics+Mind+Conjectures; 11: Goodel’s Theorem and Fundamental Limitations of Computation and Learning; 11.1: Penrose and Computability of Mathematical Understanding; 11.2: Logic and Mind; 11.3: Godel, Turing, Penrose, and Putnam; 11.4: Godel Theorem vs. Physics of Mind; Notes; Biliographical Notes; 12: Toward Physics of Consciousness; 12.1: Phenomenology of Consciousness; 12.2: Physics of Spiritual Substance. Future Directions; 12.3: Epilogue; Notes; Bibliographical Notes; Symbols and Notations; Definitions and Index; Bibliography

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