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Produktbild: Neural Networks and Intellect

Neural Networks and Intellect Using Model-Based Concepts

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Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

01.10.2000

Verlag

Oxford Academic

Seitenzahl

496

Maße (L/B/H)

24,1/19,6/3,1 cm

Gewicht

1034 g

Sprache

Englisch

ISBN

978-0-19-511162-0

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

01.10.2000

Verlag

Oxford Academic

Seitenzahl

496

Maße (L/B/H)

24,1/19,6/3,1 cm

Gewicht

1034 g

Sprache

Englisch

ISBN

978-0-19-511162-0

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: gpsr@libri.de

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  • Produktbild: Neural Networks and Intellect

    • 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