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Neural Networks and Intellect Using Model-Based Concepts

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ISBN-10: 0195111621

ISBN-13: 9780195111620

Edition: 2000

Authors: Leonid I. Perlovsky

List price: $249.99
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Description:

Neural Networks and Intellect: Using Model-Based Concepts describes a new mathematical concept of modeling field theory and its applications to a variety of problems. Examining the relationships among mathematics, computations in neural networks, signs and symbols in semiotics, and ideas of mind in psychology and philosophy, this unique text discusses deep philosophical questions in detail and relates them to mathematics and the engineering of intelligence. Ideal for courses in neural networks, modern pattern recognition, and mathematical concepts of intelligence, it will also be of interest to anyone working in a variety of fields including neural networks, AI, cognitive science, fuzzy…    
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Book details

List price: $249.99
Copyright year: 2000
Publisher: Oxford University Press, Incorporated
Publication date: 10/19/2000
Binding: Hardcover
Pages: 496
Size: 9.09" wide x 7.40" long x 1.10" tall
Weight: 2.288
Language: English

Chapters 1-7, 9, and 10 end with Notes, Bibliographical Notes, and Problems
Chapter 8 ends with Bibliographical Notes and Problems
Chapters 11 and 12 end with Notes and Bibliographical Notes
Preface
Overview: 2300 Years of Philosophy, 100 Years of Mathematical Logic, and 50 Years of Computational Intelligence
Introduction: Concepts of Intelligence
Concepts of Intelligence in Mathematics, Psychology, and Philosophy
Probability, Hypothesis Choice, Pattern Recognition, and Complexity
Prediction, Tracking, and Dynamic Models
Preview: Intelligence, Internal Model, Symbol, Emotions, and Consciousness
Mathematical Concepts of Mind
Complexity, Aristotle, and Fuzzy Logic
Nearest Neighbors and Degenerate Geometries
Gradient Learning, Back Propagation, and Feedforward Neural Networks
Rule-Based Artificial Intelligence
Concept of Internal Model
Abductive Reasoning
Statistical Learning Theory and Support Vector Machines
AI Debates Past and Future
Society of Mind
Sensor Fusion and JDL Model
Hierarchical Organization
Semiotics
Evolutionary Computation, Genetic Algorithms, and CAS
Neural Field Theories
Intelligence, Learning, and Computability
Mathematical versus Metaphysical Concepts of Mind
Prolegomenon: Plato, Antisthenes, and Artifical Intelligence
Learning from Aristotle to Maimonides
Heresy of Occam and Scientific Method
Mathematics vs Physics
Kant: Pure Spirit and Psychology
Freud vs Jung: Psychology of Philosophy
Wither We Go From Here?
Modeling field Theory: New Mathematical Theory of Intelligence with Examples of Engineering Applications
Modeling Field Theory
Internal Models, Uncertainties, and Similarities
Modeling Field Theory Dynamics
Bayesian MFT
Shannon-Einsteinian MFT
Modeling Field Theory Neural Architecture
Convergence
Learning of Structures, AIC, and SLT
Instinct of World Modeling: Knowledge Instinct
MLANS: Maximum Likelihood Adaptive Neural System for Grouping and Recognition
Grouping, Classification, and Models
Gaussian Mixture Model: Unsupervised Learning or Grouping
Combined Supervised and Unsupervised Learning
Structure Estimation
Wishart and Rician Mixture Models for Radar Image Classification
Convergence
MLANS, Physics, Biology, and Other Neural Networks
Einsteinian Neural Network
Images, Signals, and Spectra
Spectral Models
Neural Dynamics of ENN
Applications to Acoustic Transient Signals and Speech Recognition
Applications to Electromagnetic Wave Propagation in the Ionosphere
Summary
Appendix
Prediction, Tracking, and Dynamic Models
Prediction, Association, and Nonlinear Regression
Association and Tracking Using Bayesian MFT
Association and Tracking Using Shannon-Einsteinian MFT (SE-CAT)
Sensor Fusion MFT
Attention
Quantum Modeling Field Theory (QMFT)
Quantum Computing and Quantum Physics Notations
Gibbs Quantum Modeling Field System
Hamiltonian Quantum Modeling Field System
Fundamental Limitations on Learning
The Cramer-Rao Bound on Speed of Learning
Overlap Between Classes
CRB for MLANS
CRB for Concurrent Association and Tracking (CAT)
Summary: CRB for Intellect and Evolution?
Appendix: CRB Rule of Thumb for Tracking
Intelligent Systems Organization: MFT, Genetic Algorithms, and Kant
Kant, MFT, and Intelligent Systems
Emotional Machine (Toward Mathematics of Beauty)
Learning: Genetic Algorithms, MFT, and Semiosis
Futuristic Directions: Fun Stuff: Mind--Physics + Mathematics + Conjectures
Godel''s Theorems, Mind, and Machine
Penrose and Computability of Mathematical Understanding
Logic and Mind
Godel, Turing, Penrose, and Putnam
Godel Theorem vs Physics of Mind
Toward Physics of Consciousness
Phenomenology of Consciousness
Physics of Spiritual Substance: Future Direct