Addison-Wesley Publishing Company, 1991 The material from which this book was written was originally developed for a series of short courses and seminars for practicing engineers. For many of our students, the courses provided a first exposure to the technology. Some were computer-science majors with specialties in artificial intelligence, but many came from a variety of engineering backgrounds. Some were recent graduates; others held Ph.Ds. Since it was impossible to prepare separate courses tailored to individual backgrounds, we were faced with the challenge of designing material that would meet the needs of the entire spectrum of our student population. We retain that ambition for the material presented in this book.
Sign up or login using form at top of the page to download this file.
Cambridge University Press, 2009, 389 p. This important work 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. Chapters survey research on pattern classification with binary-output networks, including a discussion of...
Oxford University Press, 1996. — 504 p. — ISBN10: 0198538642 Basic book for teaching neural networks in Western universities. This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts, the book examines techniques for modeling probability density functions and the...
Study guide / monograph Publisher: Springer, -516 p., November 9, 2010 ISBN10: 3642061877ISBN13: 978-3642061875 Neural networks represent a powerful data processing technique that has reached maturity and broad application. When clearly understood and appropriately used, they are a mandatory component in the toolbox of any engineer who wants make the best use of the available...
Academic Press, 1998, -423 p. Inspired by the structure of the human brain, artificial neural networks have been widely applied to fields such as pattern recognition, optimization, coding, control, etc., because of their ability to solve cumbersome or intractable problems by learning directly from data. An artificial neural network usually consists of a large number of simple...
Academic Press, 1998, -421 p. Inspired by the structure of the human brain, artificial neural networks have been widely applied to fields such as pattern recognition, optimization, coding, control, etc., because of their ability to solve cumbersome or intractable problems by learning directly from data. An artificial neural network usually consists of a large number of simple...
Academic Press, 1998, -407 p. Inspired by the structure of the human brain, artificial neural networks have been widely applied to fields such as pattern recognition, optimization, coding, control, etc., because of their ability to solve cumbersome or intractable problems by learning directly from data. An artificial neural network usually consists of a large number of simple...