Innovative Integration
Innovative Integration provides board-level hardware products that integrate the best analog I/O and reconfigurable FPGAs to provide cost-effective solutions for challenging data acquisition and signal processing applications.
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book
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Bioinformatics : The Machine Learning Approach, Second Edition
An unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules.
The demands and opportunities for interpreting these data are expanding rapidly.
Bioinformatics is the development and application of computer methods for management, analysis, interpretation, and prediction, as well as for the design of experiments.
preview:
http://www.amazon.com
date: 8/1/2001
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book
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Creative Evolutionary Systems (With CD-ROM)
This volume shows the current state of the art, and the science, of evolutionary creativity.
It shows what can--and equally important, what can't--be done at the turn of the new millennium.
What will have been achieved by the turn of the next one is anyone's guess.
preview:
http://www.amazon.com
date: 1/15/2001
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book
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Information Theoretic Aspects of Neural Networks
Information theoretics vis-a-vis neural networks generally embodies parametric entities and conceptual bases pertinent to memory considerations and information storage, information-theoretic based cost-functions, and neurocybernetics and self-organization.
preview:
http://www.amazon.com
date: 3/3/1999
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book
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Kalman Filtering and Neural Networks
This self-contained book consists of seven chapters by expert contributors that discuss Kalman filtering as applied to the training and use of neural networks.
Although the traditional approach to the subject is almost always linear, this book recognizes and deals with the fact that real problems are most often nonlinear.
preview:
http://www.amazon.com
date: 9/1/2001
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book
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Neural Network Learning : Theoretical Foundations
This book describes recent theoretical advances in the study of artificial neural networks.
It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions.
The authors also discuss the computational complexity of neural network learning, describing a variety of hardness results, and outlining two efficient constructive learning algorithms.
preview:
http://www.amazon.com
date: 1/15/1999
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