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On Intelligence
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by
Jeff Hawkins, Sandra Blakeslee
From the inventor of the PalmPilot comes a new and compelling theory of intelligence, brain function, and the future of intelligent machines. Written with acclaimed science writer Blakeslee, this text promises to transfigure the possibilities of the technology age.
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Business @ the Speed of Thought: Succeeding in the Digital Economy
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Bill Gates, Collins Hemingway
Bill Gates draws on his own experiences as chairman of Microsoft and offers his best insights into how companies can become successful. He advocates that companies develop what he calls "digital nervous systems"--a way of using computers to maximize the flow of information for profit, and provides examples from companies he thinks have done things ...
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Statistical Mechanics of Learning
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by
A Engel, C Van Den Broeck
Learning is one of the things that humans do naturally, and it has always been a challenge for us to understand the process. Nowadays this challenge has another dimension as we try to build machines that are able to learn and to undertake tasks such as datamining, image processing and pattern recognition. We can formulate a simple framework, ...
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Neural Networks for Pattern Recognition
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by
Christopher M Bishop
This book provides the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts of pattern recognition, the book describes techniques for modelling probability density functions, and discusses the properties and relative merits of the multi-layer ...
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Cellular Neural Networks and Visual Computing: Foundations and Applications
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Leon O Chua, Tamas Roska
Cellular Nonlinear/neural Network (CNN) technology is both a revolutionary concept and an experimentally proven new computing paradigm. Analogic cellular computers based on CNNs are set to change the way analog signals are processed and are paving the way to an entire new analog computing industry. This unique undergraduate level textbook includes ...
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Bayesian Networks and Decision Graphs
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by
Finn B Jensen
Probabilistic graphical models and decision graphs are powerful modeling tools for reasoning and decision making under uncertainty. As modeling languages they allow a natural specification of problem domains with inherent uncertainty, and from a computational perspective they support efficient algorithms for automatic construction and query ...
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C++ Neural Networks and Fuzzy Logic
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by
Valluru B Rao
This text teaches the core concepts of fuzzy logic and neural network technologies, including back propagation and adaptive resonance theory. It includes practical applications demonstrating pattern recognition, optimization and financial modelling. The disk includes all the source code discussed.
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Neural and Adaptive Systems: Fundamentals Through Simulations
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by
Jos? C Principe, Neil R Euliano, W Curt Lefebvre
Develop New Insight into the Behavior of Adaptive Systems This one-of-a-kind interactive book and CD-ROM will help you develop a better understanding of the behavior of adaptive systems. Developed as part of a project aimed at innovating the teaching of adaptive systems in science and engineering, it unifies the concepts of neural networks and ...
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Fundamentals of Neural Networks: Architectures, Algorithms and Applications
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by
Laurene V Fausett
An exceptionally clear, thorough introduction to neural networks written at an elementary level. Written with the beginning student in mind, the text features systematic discussions of all major neural networks and fortifies the reader's understanding with many examples. An exceptionally clear, thorough introduction to neural networks written at ...
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Cognizers: Neural Networks and Machines That Think
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by
R. Colin Johnson, Chappell Brown
One of the most dramatic developments in computer science has been the effort to create machines that duplicate the neurotransmitter biology of the human brain. Describing for the general reader how human neural networks work, the authors explain in this text how this new technology could be the breakthrough that makes artificial intelligence a ...
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Neural Networks and Learning Machines
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by
Simon Haykin
For graduate-level neural network courses offered in the departments of Computer Engineering, Electrical Engineering, and Computer Science. Renowned for its thoroughness and readability, this well-organized and completely up-to-date text remains the most comprehensive treatment of neural networks from an engineering perspective. Refocused, revised ...
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The Harmonic Mind, Volume 1: From Neural Computation to Optimality-Theoretic Grammar: Cognitive Architecture
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Paul Smolensky, Geraldine Legendre
An integrated connectionist/symbolic architecture of the mind/brain, applied to neural/genomic realisation of grammar; aequisition, processing, and typology in phonology and syntax; and foundations of cognitive explanation. Despite their apparently divergent accounts of higher cognition, cognitive theories based on neural computation and those ...
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The Harmonic Mind, Volume 2: From Neural Computation to Optimality-Theoretic Grammar: Linguistic and Philosophical Implications
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by
Paul Smolensky, Geraldine Legendre
An integrated connectionist/symbolic architecture of the mind/brain, applied to neural/genomic realisation of grammar; aequisition, processing, and typology in phonology and syntax; and foundations of cognitive explanation. Despite their apparently divergent accounts of higher cognition, cognitive theories based on neural computation and those ...
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Building Neural Networks
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by
David M Skapura, Peter S Gordon (Editor)
This practical introduction describes the kinds of real-world problems neural network technology can solve. Surveying a range of neural network applications, the book demonstrates the construction and operation of artificial neural systems. Through numerous examples, the author explains the process of building neural-network applications that ...
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Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence
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by
Bart Kosko, Ph.D.
Combining neural networks and fuzzy systems, this presents neural networks as trainable dynamical systems and develops mechanisms and principles of adaption, self-organization, covergence and global stability. It also includes the new geometric theory of fuzzy sets, systems and associative memories. Kosko shows how to apply fuzzy theory to ...
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Neural Network Design
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by
Beale Hagan Demuth, Mark Beale, Martin T Hagan
This guide to neural networks includes worked problems in each chapter, and supporting software demonstrations. The authors of this work also developed the neural networks toolbox (for MathWorks) and the fuzzy systems toolbox (for PWS).
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Neural Networks in C++: An Object-Oriented Framework for Building Connectionist Systems
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by
Adam Blum
Following an introduction to neural networks, this text describes the basics of object-orientation for potential users. Sample applications are developed and demonstrated in a variety of areas, including image recognition, text processing and forecasting. Program listings for the these applications are provided in their entirety using Borland C++ ...
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Flexible Neuro-Fuzzy Systems
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by
Leszek Rutkowski
"Flexible Neuro-Fuzzy Systems" is the first professional literature about the new class of powerful, flexible fuzzy systems. The author incorporates various flexibility parameters to the construction of neuro-fuzzy systems. This approach dramatically improves their performance, allowing the systems to perfectly represent the pattern encoded in ...
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Neural Network and Fuzzy Logic Applications in C/C++
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by
Stephen T Welstead
Using an engineering and science perspective, it explores diverse neural network, fuzzy logic and genetic algorithm techniques, plus developing applications best suited for each of the methods discussed. Sample results are described and judgment made as to how well each application worked. The book/disk set includes an object-oriented user ...
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Enchanted Looms: Conscious Networks in Brains and Computers
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by
Rodney M J Cotterill
The title of this book was inspired by a passage in Charles Sherrington's Man on his Nature. When that famous physiologist died in 1952, the prospects for a scientific explanation of consciousness seemed remote. Enchanted Looms shows how the situation has changed dramatically, and provides what is probably the most wide-ranging account of the ...
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Neural Networks and Psychopathology: Connectionist Models in Practice and Research
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by
Dan J Stein, M.D. (Editor), Jacques Ludik (Editor)
Research on connectionist models is one of the most exciting areas in cognitive science, and neural network models of psychopathology have immediate theoretical and empirical appeal. The contributors to this study review theoretical, historical and clinical issues, including the contribution of neural network models to diagnosis, pharmacotherapy ...
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Imitation of Life: How Biology Is Inspiring Computing
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by
Nancy Forbes
As computers and the tasks they perform become increasingly complex, researchers are looking to nature - as model and as metaphor - for inspiration. The organisation and behaviour of biological organisms present scientists with an invitation to reinvent computing for the complex tasks of the future. In Imitation of Life Nancy Forbes surveys the ...
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An Introduction to the Modeling of Neural Networks
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by
Peretto
This text is a beginning graduate-level introduction to neural networks, focussing on current theoretical models, examining what these models can reveal about how the brain functions, and discussing the ramifications for psychology, artificial intelligence and the construction of a new generation of intelligent computers. The book is divided into ...
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Darwin Among the Machines: The Evolution of Global Intelligence
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by
George Dyson
Darwin Among the Machines retraces the steps that led us into the digital wilderness, no less wild for being a universe of our own device. Introducing a cast of known and unknown characters, George B. Dyson traces the course of the information revolution, illuminating the lives and work of visionaries-from the time of Thomas Hobbes to the time ...
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Neural, Novel & Hybrid Algorithms for Time Series Prediction
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by
Timothy Masters
Time series prediction has become a much-relied upon forecasting technique among economists, process control engineers, and stock market players alike. This book is a non-theoretical 'nuts and bolts' guide that gives you, the programmer, the tools, techniques, and implementation details you need for successful time series prediction. It combines ...
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