Pattern Recognition and Neural Networks Book

Pattern Recognition and Neural Networks


  • Author : Brian D. Ripley
  • Publisher : Cambridge University Press
  • Release Date : 2007
  • Genre: Computers
  • Pages : 420
  • ISBN 10 : 0521717701
  • Total Read : 71
  • File Size : 13,6 Mb

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Pattern Recognition and Neural Networks Summary:

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

Neural Networks for Pattern Recognition Book

Neural Networks for Pattern Recognition


  • Author : Christopher M. Bishop
  • Publisher : Oxford University Press
  • Release Date : 1995-11-23
  • Genre: Computers
  • Pages : 501
  • ISBN 10 : 9780198538646
  • Total Read : 75
  • File Size : 11,9 Mb

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Neural Networks for Pattern Recognition Summary:

Statistical pattern recognition; Probability density estimation; Single-layer networks; The multi-layer perceptron; Radial basis functions; Error functions; Parameter optimization algorithms; Pre-processing and feature extraction; Learning and generalization; Bayesian techniques; Appendix; References; Index.

Neural Networks for Pattern Recognition Book

Neural Networks for Pattern Recognition


  • Author : Albert Nigrin
  • Publisher : MIT Press
  • Release Date : 1993
  • Genre: Computers
  • Pages : 450
  • ISBN 10 : 0262140543
  • Total Read : 65
  • File Size : 9,6 Mb

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Neural Networks for Pattern Recognition 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.

Pattern Recognition with Neural Networks in C   Book
Score: 4
From 2 Ratings

Pattern Recognition with Neural Networks in C


  • Author : Abhijit S. Pandya
  • Publisher : CRC Press
  • Release Date : 1995-10-17
  • Genre: Computers
  • Pages : 434
  • ISBN 10 : 0849394627
  • Total Read : 59
  • File Size : 18,6 Mb

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Pattern Recognition with Neural Networks in C Summary:

The addition of artificial neural network computing to traditional pattern recognition has given rise to a new, different, and more powerful methodology that is presented in this interesting book. This is a practical guide to the application of artificial neural networks. Geared toward the practitioner, Pattern Recognition with Neural Networks in C++ covers pattern classification and neural network approaches within the same framework. Through the book's presentation of underlying theory and numerous practical examples, readers gain an understanding that will allow them to make judicious design choices rendering neural application predictable and effective. The book provides an intuitive explanation of each method for each network paradigm. This discussion is supported by a rigorous mathematical approach where necessary. C++ has emerged as a rich and descriptive means by which concepts, models, or algorithms can be precisely described. For many of the neural network models discussed, C++ programs are presented for the actual implementation. Pictorial diagrams and in-depth discussions explain each topic. Necessary derivative steps for the mathematical models are included so that readers can incorporate new ideas into their programs as the field advances with new developments. For each approach, the authors clearly state the known theoretical results, the known tendencies of the approach, and their recommendations for getting the best results from the method. The material covered in the book is accessible to working engineers with little or no explicit background in neural networks. However, the material is presented in sufficient depth so that those with prior knowledge will find this book beneficial. Pattern Recognition with Neural Networks in C++ is also suitable for courses in neural networks at an advanced undergraduate or graduate level. This book is valuable for academic as well as practical research.

Adaptive Pattern Recognition and Neural Networks Book

Adaptive Pattern Recognition and Neural Networks


  • Author : Yoh-Han Pao
  • Publisher : Addison Wesley Publishing Company
  • Release Date : 1989
  • Genre: Computers
  • Pages : 309
  • ISBN 10 : UOM:39015012010578
  • Total Read : 63
  • File Size : 7,6 Mb

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Adaptive Pattern Recognition and Neural Networks Summary:

A coherent introduction to the basic concepts of pattern recognition, incorporating recent advances from AI, neurobiology, engineering, and other disciplines. Treats specifically the implementation of adaptive pattern recognition to neural networks. Annotation copyright Book News, Inc. Portland, Or.

Artificial Neural Networks in Pattern Recognition Book

Artificial Neural Networks in Pattern Recognition


  • Author : Luca Pancioni
  • Publisher : Springer
  • Release Date : 2018-08-29
  • Genre: Computers
  • Pages : 408
  • ISBN 10 : 9783319999784
  • Total Read : 79
  • File Size : 7,9 Mb

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Artificial Neural Networks in Pattern Recognition Summary:

This book constitutes the refereed proceedings of the 8th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2018, held in Siena, Italy, in September 2018. The 29 revised full papers presented together with 2 invited papers were carefully reviewed and selected from 35 submissions. The papers present and discuss the latest research in all areas of neural network- and machine learning-based pattern recognition. They are organized in two sections: learning algorithms and architectures, and applications. Chapter "Bounded Rational Decision-Making with Adaptive Neural Network Priors" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

Neural Networks for Applied Sciences and Engineering Book
Score: 4
From 1 Ratings

Neural Networks for Applied Sciences and Engineering


  • Author : Sandhya Samarasinghe
  • Publisher : CRC Press
  • Release Date : 2016-04-19
  • Genre: Computers
  • Pages : 570
  • ISBN 10 : 9781420013061
  • Total Read : 81
  • File Size : 14,7 Mb

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Neural Networks for Applied Sciences and Engineering Summary:

In response to the exponentially increasing need to analyze vast amounts of data, Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition provides scientists with a simple but systematic introduction to neural networks. Beginning with an introductory discussion on the role of neural networks in

From Statistics to Neural Networks Book

From Statistics to Neural Networks


  • Author : Vladimir Cherkassky
  • Publisher : Springer Science & Business Media
  • Release Date : 2012-12-06
  • Genre: Computers
  • Pages : 394
  • ISBN 10 : 9783642791192
  • Total Read : 89
  • File Size : 5,6 Mb

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From Statistics to Neural Networks 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.