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Produktbild: Neural Networks and Intellect

Neural Networks and Intellect Using Model-Based Concepts

324,99 €

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Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

01.10.2000

Abbildungen

numerous line figures

Verlag

Oxford Academic

Seitenzahl

496

Maße (L/B/H)

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

Gewicht

1111 g

Sprache

Englisch

ISBN

978-0-19-511162-0

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

01.10.2000

Abbildungen

numerous line figures

Verlag

Oxford Academic

Seitenzahl

496

Maße (L/B/H)

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

Gewicht

1111 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