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Sunday, October 4, 2020 | History

12 edition of Pattern recognition and neural networks found in the catalog.

Pattern recognition and neural networks

by Brian D. Ripley

  • 180 Want to read
  • 26 Currently reading

Published by Cambridge University Press in Cambridge, New York .
Written in English

    Subjects:
  • Neural networks (Computer science),
  • Pattern recognition systems.

  • Edition Notes

    Includes bibliographical references (p. [355]-390) and indexes.

    StatementB.D. Ripley.
    Classifications
    LC ClassificationsQA76.87 .R56 1996
    The Physical Object
    Paginationxi, 403 p. :
    Number of Pages403
    ID Numbers
    Open LibraryOL792651M
    ISBN 100521460867
    LC Control Number95025223

    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. @article{osti_, title = {Adaptive pattern recognition and neural networks}, author = {Pao, Yohhan.}, abstractNote = {The application of neural-network computers to pattern-recognition tasks is discussed in an introduction for advanced students. Chapters are devoted to the nature of the pattern-recognition task, the Bayesian approach to the estimation of class .

    Written for courses in pattern recognition and neural networks, this book discusses the theory and practical application of neural networks. Topics covered include parameter optimization algorithms, density modeling, single layer networks, multi-layer perceptron, bayesian techniques, and gaussian processes. Ripley brings together two crucial ideas in pattern recognition: statistical methods and machine learning via neural networks. He brings unifying principles to the fore, and reviews the state of.

    Although primarily a textbook for teaching undergraduate and postgraduate courses in pattern recognition and neural networks, this book will also be of interest to practitioners and researchers who can use the toolbox to develop application solutions and new models. Pattern recognition has long been studied in relation to many different (and mainly unrelated) applications, such as remote sensing, computer vision, space research, and medical imaging. In this book Professor Ripley brings together two crucial ideas in pattern recognition; statistical methods and machine learning via neural networks/5(6).


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Pattern recognition and neural networks by Brian D. Ripley Download PDF EPUB FB2

Pattern recognition has long been studied in relation to many different (and mainly unrelated) applications, such as remote sensing, computer vision, space research, and medical imaging.

In this book Professor Ripley brings together two crucial ideas in pattern recognition; statistical methods and machine learning via neural by: The book starts with the introduction to the problems neural nets are to be applied to - pattern recognition task.

It proceeds to the elements of statistical decision theory, then goes up to linear discriminant analysis and perceptrons, then up you go to feed-forward neural nets.

Non-parametric models and tree-based classifiers are covered next/5(6). This book uses tools from statistical decision theory and computational learning theory to create a rigorous foundation for the theory of neural networks. On the theoretical side, Pattern Recognition and Neural Networks emphasizes probability and statistics.

Almost all the results have proofs that are often original/5(6). This book is a reliable account of the statistical framework for pattern recognition and machine learning. With unparalleled coverage and a wealth of case-studies this book gives valuable insight into both the theory and the enormously diverse applications (which can be found in remote sensing, astrophysics, engineering and medicine, for example).Cited by: This book is one of the most up-to-date and cutting-edge texts available on the rapidly growing application area of neural networks.

Neural Networks and Pattern Recognition focuses on the use of neural networksin pattern recognition, a very important application area for neural networks by: This is a very well-written book giving a practical, hands-on overview of neural networks (NN's) as applied to pattern recognition.

Looney includes most of the "modern" neural networks that perform best on pattern recognition problems (no Cited by: Pattern Recognition and Neural Networks.

This book is a reliable account of the statistical framework for pattern recognition and machine learning. This book is one of the most up-to-date and cutting-edge texts available on the rapidly growing application area of neural networks.

Neural Networks and Pattern Recognition focuses on the use of neural networksin pattern recognition, a very important application area for neural networks technology. The contributors are widely known and highly respected researchers.

and medical imaging. In this book Professor Ripley brings together two crucial ideas in pattern recognition; statistical methods and machine learning via neural networks. Unifying principles are brought to the fore, and the author gives an overview of the state of the subject.

Many examples are included to illustrate real problems in pattern. Pattern recognition is extremely widely used, often under the names of `classification', `diagnosis' or `learning from examples'. The methods are often very successful, and this book explains why.

It is an in-depth study of methods for pattern recognition drawn from engineering, statistics, machine learning and neural networks. This book both introduces and relates the basic concepts of pattern recognition and neural networks.

The first part provides a much-needed, current, and coherent view of 4/5. Pattern recognition has long been studied in relation to many different (and mainly unrelated) applications, such as remote sensing, computer vision, space research, and medical imaging.

In this book Professor Ripley brings together two crucial ideas in pattern recognition; statistical methods and machine learning via neural networks/5(17). 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 modelling probability density functions and the properties and merits of the multi-layer perceptron and radial basis function network models.

Description This book is one of the most up-to-date and cutting-edge texts available on the rapidly growing application area of neural networks. Neural Networks and Pattern Recognition focuses on the use of neural networksin pattern recognition, a very important application area for neural networks technology.

A Statistical Approach to Neural Networks for Pattern Recognition successfully connects logistic regression and linear discriminant analysis, thus making it a critical reference and self-study guide for students and professionals alike in the fields of mathematics, statistics, computer science, and electrical engineering.

‘Pattern Recognition and Neural Networks’ by B.D. Ripley Cambridge University Press,ISBN These complements provide further details, and references which appeared (or came to my attention) after the book was completed in June Minor corrections can be found in the Errata list.

Chapter 1: Introduction Page 4. Book Abstract: 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.

On the theoretical side, Pattern Recognition and Neural Networks emphasizes probability and statistics. Almost all the results have proofs that are often original. On the application side, the emphasis is on pattern recognition.

Most of the examples are from real world problems. In addition to the more common types of networks, the book has. Now the book is published, these files will remain viewable on this website.

The same copyright rules will apply to the online copy of the book as apply to normal books. [e.g., copying the whole book onto paper is not permitted.] History: Draft - March 14 Draft - April 4 Draft - April 9 Draft - April 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.

Ripley brings together two crucial ideas in pattern recognition: statistical methods and machine learning via neural networks. He brings unifying principles to the fore, and reviews the state of the subject.

Ripley also includes many examples to illustrate real problems in pattern recognition and how to overcome them.Pattern Recognition and Neural Networks by Ripley, Brian D. and a great selection of related books, art and collectibles available now at - Pattern Recognition and Neural Networks by Ripley, Brian D - AbeBooks.

Pattern recognition has long been studied in relation to many different (and mainly unrelated) applications, such as remote sensing, computer vision, space research, and medical imaging. In this book Professor Ripley brings together two crucial ideas in pattern recognition; statistical methods and machine learning via neural networks.