Topics
Complexity/Complex Systems/Dynamic Systems
Computational Neuroscience
Connectionism
Evolutionary Computation
Learning Systems
Machine Learning and Vision
Language
Linguistic Universal and Universal Grammar
Linguistics
Metaphor
Natural Language
Semantics
Sign Language
Syntax
Neural Development
Neural Networks
Neural Plasticity
Neuron
Visual Anatomy and Physiology
Neural Network Learning and Expert Systems
Neural Network Learning and Expert Systems is the first book to present a unified and in-depth development of neural network learning algorithms and neural network expert systems. Especially suitable for students and researchers in computer science, engineering, and psychology, this text and reference provides a systematic development of neural network learning algorithms from a computational perspective, coupled with an extensive exploration of neural network expert systems which shows how the power of neural network learning can be harnessed to generate expert systems automatically.
Features include a comprehensive treatment of the standard learning algorithms (with many proofs), along with much original research on algorithms and expert systems. Additional chapters explore constructive algorithms, introduce computational learning theory, and focus on expert system applications to noisy and redundant problems.
For students there is a large collection of exercises, as well as a series of programming projects that lead to an extensive neural network software package. All of the neural network models examined can be implemented using standard programming languages on a microcomputer.