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Title Neural Networks and Pattern Recognition
Author Collectif
Publisher Academic Press
Release 1998
Category Computers
Total Pages 351
ISBN 9780125264204
Language English, Spanish, and French
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Book Summary:

Pulse-coupled neural networks; A neural network model for optical flow computation; Temporal pattern matching using an artificial neural network; Patterns of dynamic activity and timing in neural network processing; A macroscopic model of oscillation in ensembles of inhibitory and excitatory neurons; Finite state machines and recurrent neural networks: automata and dynamical systems approaches; biased random-waldk learning; a neurobiological correlate to trial-and-error; Using SONNET 1 to segment continuous sequences of items; On the use of high-level petri nets in the modeling of biological neural networks; Locally recurrent networks: the gmma operator, properties, and extensions.

Title Pattern Recognition and Neural Networks
Author Brian D. Ripley
Publisher Cambridge University Press
Release 2007
Category Computers
Total Pages 403
ISBN 9780521717700
Language English, Spanish, and French
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Book Summary:

This 1996 book explains the statistical framework for pattern recognition and machine learning, now in paperback.

Neural Networks for Pattern Recognition by Christopher M. Bishop

Title Neural Networks for Pattern Recognition
Author Christopher M. Bishop
Publisher Oxford University Press
Release 1995-11-23
Category Computers
Total Pages 482
ISBN 0198538642
Language English, Spanish, and French
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Book Summary:

`Readers will emerge with a rigorous statistical grounding in the theory of how to construct and train neural networks in pattern recognition' New Scientist

Title Artificial Neural Networks in Pattern Recognition
Author Friedhelm Schwenker
Publisher Springer
Release 2016-09-08
Category Computers
Total Pages 335
ISBN 3319461826
Language English, Spanish, and French
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Book Summary:

This book constitutes the refereed proceedings of the 7th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2016, held in Ulm, Germany, in September 2016. The 25 revised full papers presented together with 2 invited papers were carefully reviewed and selected from 32 submissions for inclusion in this volume. The workshop will act as a major forum for international researchers and practitioners working in all areas of neural network- and machine learning-based pattern recognition to present and discuss the latest research, results, and ideas in these areas.

Title Pattern Recognition and Neural Networks
Author Ludmila Kuncheva
Publisher Lulu.com
Release
Category
Total Pages
ISBN 0244232520
Language English, Spanish, and French
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Book Summary:

Title Pattern Recognition with Neural Networks in C
Author Abhijit S. Pandya
Publisher CRC Press
Release 2019-12-17
Category
Total Pages 432
ISBN 9780367448875
Language English, Spanish, and French
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Book Summary:

The addition of artificial network computing to traditional pattern recognition has given rise to a new, different, and more powerful methodology that is presented in this practical guide to the application of artificial neural networks. The material covered in the book is accessible to working engineers with little or no explicit background in neural networks.

Title Neural Networks for Pattern Recognition
Author Albert Nigrin
Publisher MIT Press
Release 1993
Category Computers
Total Pages 413
ISBN 9780262140546
Language English, Spanish, and French
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Book Summary:

In a simple and accessible way it extends embedding field theory into areas of machine intelligence that have not been clearly dealt with before. Neural Networks for Pattern Recognition takes the pioneering work in artificial neural networks by Stephen Grossberg and his colleagues to a new level. In a simple and accessible way it extends embedding field theory into areas of machine intelligence that have not been clearly dealt with before. Following a tutorial of existing neural networks for pattern classification, Nigrin expands on these networks to present fundamentally new architectures that perform realtime pattern classification of embedded and synonymous patterns and that will aid in tasks such as vision, speech recognition, sensor fusion, and constraint satisfaction. Nigrin presents the new architectures in two stages. First he presents a network called Sonnet 1 that already achieves important properties such as the ability to learn and segment continuously varied input patterns in real time, to process patterns in a context sensitive fashion, and to learn new patterns without degrading existing categories. He then removes simplifications inherent in Sonnet 1 and introduces radically new architectures. These architectures have the power to classify patterns that may have similar meanings but that have different external appearances (synonyms). They also have been designed to represent patterns in a distributed fashion, both in short-term and long-term memory.

Title Artificial Neural Networks in Pattern Recognition
Author Friedhelm Schwenker
Publisher Springer
Release 2006-08-29
Category Computers
Total Pages 302
ISBN 3540379525
Language English, Spanish, and French
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Book Summary:

This book constitutes the refereed proceedings of the Second IAPR Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2006, held in Ulm, Germany in August/September 2006. The 26 revised papers presented were carefully reviewed and selected from 49 submissions. The papers are organized in topical sections on unsupervised learning, semi-supervised learning, supervised learning, support vector learning, multiple classifier systems, visual object recognition, and data mining in bioinformatics.

From Statistics to Neural Networks by Vladimir Cherkassky

Title From Statistics to Neural Networks
Author Vladimir Cherkassky
Publisher Springer Science & Business Media
Release 2012-12-06
Category Computers
Total Pages 394
ISBN 3642791190
Language English, Spanish, and French
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Book Summary:

The NATO Advanced Study Institute From Statistics to Neural Networks, Theory and Pattern Recognition Applications took place in Les Arcs, Bourg Saint Maurice, France, from June 21 through July 2, 1993. The meeting brought to gether over 100 participants (including 19 invited lecturers) from 20 countries. The invited lecturers whose contributions appear in this volume are: L. Almeida (INESC, Portugal), G. Carpenter (Boston, USA), V. Cherkassky (Minnesota, USA), F. Fogelman Soulie (LRI, France), W. Freeman (Berkeley, USA), J. Friedman (Stanford, USA), F. Girosi (MIT, USA and IRST, Italy), S. Grossberg (Boston, USA), T. Hastie (AT&T, USA), J. Kittler (Surrey, UK), R. Lippmann (MIT Lincoln Lab, USA), J. Moody (OGI, USA), G. Palm (U1m, Germany), B. Ripley (Oxford, UK), R. Tibshirani (Toronto, Canada), H. Wechsler (GMU, USA), C. Wellekens (Eurecom, France) and H. White (San Diego, USA). The ASI consisted of lectures overviewing major aspects of statistical and neural network learning, their links to biological learning and non-linear dynamics (chaos), and real-life examples of pattern recognition applications. As a result of lively interactions between the participants, the following topics emerged as major themes of the meeting: (1) Unified framework for the study of Predictive Learning in Statistics and Artificial Neural Networks (ANNs); (2) Differences and similarities between statistical and ANN methods for non parametric estimation from examples (learning); (3) Fundamental connections between artificial learning systems and biological learning systems.

Title Artificial Neural Networks in Pattern Recognition
Author Lionel Prevost
Publisher Springer Science & Business Media
Release 2008-06-25
Category Business & Economics
Total Pages 318
ISBN 3540699384
Language English, Spanish, and French
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Book Summary:

This book constitutes the refereed proceedings of the Third TC3 IAPR Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2008, held in Paris, France, in July 2008. The 18 revised full papers and 11 revised poster papers presented were carefully reviewed and selected from 57 submissions. The papers combine many ideas from machine learning, advanced statistics, signal and image processing for solving complex real-world pattern recognition problems. The papers are organized in topical sections on unsupervised learning, supervised learning, multiple classifiers, applications, and feature selection.

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