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This comprehensive bibliography provides a functional, flexible tool for researchers and engineers in neurocomputing.
Handbook of Neural Computing Applications is a collection of articles that deals with neural networks. Some papers review the biology of neural networks, their type and function (structure, dynamics, and learning) and compare a back-propagating perceptron with a Boltzmann machine, or a Hopfield network with a Brain-State-in-a-Box network. Other papers deal with specific neural network types, and also on selecting, configuring, and implementing neural networks. Other papers address specific applications including neurocontrol for the benefit of control engineers and for neural networks researchers. Other applications involve signal processing, spatio-temporal pattern recognition, medical diagnoses, fault diagnoses, robotics, business, data communications, data compression, and adaptive man-machine systems. One paper describes data compression and dimensionality reduction methods that have characteristics, such as high compression ratios to facilitate data storage, strong discrimination of novel data from baseline, rapid operation for software and hardware, as well as the ability to recognized loss of data during compression or reconstruction. The collection can prove helpful for programmers, computer engineers, computer technicians, and computer instructors dealing with many aspects of computers related to programming, hardware interface, networking, engineering or design.
This book for nonspecialists clearly explains major algorithms and demystifies the rigorous math involved in neural networks. Uses a step-by-step approach for implementing commonly used paradigms.
This book is a beginning graduate-level introduction to neural networks which is divided into four parts.
The Biological Literature to An Uncertainty Principle for Information Seeking: A Qualitative Approach
A survey of products and research projects in the field of highly parallel, optical and neural computers in the USA. It covers operating systems, language projects and market analysis, as well as optical computing devices and optical connections of electronic parts.
Since its founding in 1989 by Terrence Sejnowski, Neural Computation has become the leading journal in the field. Foundations of Neural Computation collects, by topic, the most significant papers that have appeared in the journal over the past nine years. This volume of Foundations of Neural Computation, on unsupervised learning algorithms, focuses on neural network learning algorithms that do not require an explicit teacher. The goal of unsupervised learning is to extract an efficient internal representation of the statistical structure implicit in the inputs. These algorithms provide insights into the development of the cerebral cortex and implicit learning in humans. They are also of interest to engineers working in areas such as computer vision and speech recognition who seek efficient representations of raw input data.
For any research field to have a lasting impact, there must be a firm theoretical foundation. Neural networks research is no exception. Some of the founda tional concepts, established several decades ago, led to the early promise of developing machines exhibiting intelligence. The motivation for studying such machines comes from the fact that the brain is far more efficient in visual processing and speech recognition than existing computers. Undoubtedly, neu robiological systems employ very different computational principles. The study of artificial neural networks aims at understanding these computational prin ciples and applying them in the solutions of engineering problems. Due to the recent advances in both device technology and computational science, we are currently witnessing an explosive growth in the studies of neural networks and their applications. It may take many years before we have a complete understanding about the mechanisms of neural systems. Before this ultimate goal can be achieved, an swers are needed to important fundamental questions such as (a) what can neu ral networks do that traditional computing techniques cannot, (b) how does the complexity of the network for an application relate to the complexity of that problem, and (c) how much training data are required for the resulting network to learn properly? Everyone working in the field has attempted to answer these questions, but general solutions remain elusive. However, encouraging progress in studying specific neural models has been made by researchers from various disciplines.
Here is a unique volume on the relationship between sci-tech materials and trade literature, commonly called manufacturers’catalogs. Because very little has been published about the value and nature of trade literature in regard to sci-tech libraries, this volume will be important in informing librarians about a little-known segment of the larger picture of sci-tech information sources, thus adding to the value of their services to their clients. The Role of Trade Literature in Sci-Tech Libraries addresses the problems of handling sci-tech trade literature in a corporate technical library, a large public library, and a government library devoted to American history. Experts offer practical advice on selecting and organizing trade literature and on managing the growth and extent of a collection of trade literature. They discuss modern literature and older publications, which often have great historical value. Libraries that collect both old and new materials are identified, as are publishers of trade literature. The book also focuses on how a publisher of classic trade literature views its role. Of interest to any professional in the information science field, this practical volume will be particularly valuable to managers of corporate sci-tech libraries, managers of library collections in Americana, and beginning librarians who are learning about trade literature.