English 1993 Academic Press 504 p. This text serves as a cookbook for neural network solutions to practical problems using C++. It will enable those with moderate programming experience to select a neural network model appropriate to solving a particular problem, and to produce a working program implementing that network. The book provides guidance along the entire problem-solving path, including designing the training set, preprocessing variables, training and validating the network, and evaluating its performance. Though the book is not intended as a general course in neural networks, no background in neural works is assumed and all models are presented from the ground up. The principle focus of the book is the three layer feedforward network, for more than a decade as the workhorse of professional arsenals. Other network models with strong performance records are also included. Bound in the book is an IBM diskette that includes the source code for all programs in the book. Much of this code can be easily adapted to C compilers. In addition, the operation of all programs is thoroughly discussed both in the text and in the comments within the code to facilitate translation to other languages.
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Springer, 2011, -116 p. This brief monograph is the first one to deal exclusively with the quantitative approximation by artificial neural networks to the identity-unit operator. Here we study with rates the approximation properties of the right sigmoidal and hyperbolic tangent artificial neural network positive linear operators. In particular we study the degree of...
Imperial College Press, 2007, -322 p. The area of Neural computing that we shall discuss in this book represents a combination of techniques of classical optimization, statistics, and information theory. Neural network was once widely called artificial neural networks, which represented how the emerging technology was related to artificial intelligence. It once was a topic that...
MIT Press, 2016. — 802 p. A comprehensive introduction to neural networks and deep learning by leading researchers of this field. Written for two main target audiences: university students (undergraduate or graduate) learning about machine learning, and software engineers. This is a PDF compilation of online book (www.deeplearningbook.org) Who Should Read This Book? Historical...
3rd Edition. — World Scientific, 2013. — 363 p. — ISBN: 978-9814522731. Artificial neural networks are most suitable for solving problems that are complex, ill-defined, highly nonlinear, of many and different variables, and/or stochastic. Such problems are abundant in medicine, in finance, in security and beyond. This volume covers the basic theory and architecture of the major...
CRC Press, 1995, -326 p. Why do we feel a need to write a book about pattern recognition when many excellent books are already available on this classical topic? The answer lies in the depth of our coverage of neural networks as natural pattern classifiers and clusterers. Artificial neural network computing has emerged as an extremely active research area with a central focus...
Cambridge University Press, 2007. — 1262 p. William H. Press - Raymer Chair in Computer Sciences and Integrative Biology The University of Texas at Austin. Saul A. Teukolsky - Hans A. Bethe Professor of Physics and Astrophysics Cornell University. William T. Vetterling - Research Fellow and Director of Image Science ZINK Imaging, LLC. Brian P. Flannery - Science, Strategy and...