Artificial Neural Networks And Statistical Pattern Recognition

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Artificial Neural Networks and Statistical Pattern Recognition

I.K. Sethi,Anil K Jain
Artificial Neural Networks and Statistical Pattern Recognition Book PDF

Author : I.K. Sethi,Anil K Jain
Publisher : Elsevier
Page : 286 pages
File Size : 48,5 Mb
Release : 2014-06-28
Category : Computers
ISBN : 9781483297873

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Artificial Neural Networks and Statistical Pattern Recognition by I.K. Sethi,Anil K Jain Pdf

With the growing complexity of pattern recognition related problems being solved using Artificial Neural Networks, many ANN researchers are grappling with design issues such as the size of the network, the number of training patterns, and performance assessment and bounds. These researchers are continually rediscovering that many learning procedures lack the scaling property; the procedures simply fail, or yield unsatisfactory results when applied to problems of bigger size. Phenomena like these are very familiar to researchers in statistical pattern recognition (SPR), where the curse of dimensionality is a well-known dilemma. Issues related to the training and test sample sizes, feature space dimensionality, and the discriminatory power of different classifier types have all been extensively studied in the SPR literature. It appears however that many ANN researchers looking at pattern recognition problems are not aware of the ties between their field and SPR, and are therefore unable to successfully exploit work that has already been done in SPR. Similarly, many pattern recognition and computer vision researchers do not realize the potential of the ANN approach to solve problems such as feature extraction, segmentation, and object recognition. The present volume is designed as a contribution to the greater interaction between the ANN and SPR research communities.

Neural Networks for Pattern Recognition

Christopher M. Bishop
Neural Networks for Pattern Recognition Book PDF

Author : Christopher M. Bishop
Publisher : Oxford University Press
Page : 501 pages
File Size : 48,7 Mb
Release : 1995-11-23
Category : Computers
ISBN : 9780198538646

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Neural Networks for Pattern Recognition by Christopher M. Bishop Pdf

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.

Pattern Recognition and Neural Networks

Brian D. Ripley
Pattern Recognition and Neural Networks Book PDF

Author : Brian D. Ripley
Publisher : Cambridge University Press
Page : 420 pages
File Size : 40,6 Mb
Release : 2007
Category : Computers
ISBN : 0521717701

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Pattern Recognition and Neural Networks by Brian D. Ripley Pdf

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

Statistical Pattern Recognition

Andrew R. Webb
Statistical Pattern Recognition Book PDF

Author : Andrew R. Webb
Publisher : John Wiley & Sons
Page : 516 pages
File Size : 48,5 Mb
Release : 2003-07-25
Category : Mathematics
ISBN : 9780470854785

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Statistical Pattern Recognition by Andrew R. Webb Pdf

Statistical pattern recognition is a very active area of study andresearch, which has seen many advances in recent years. New andemerging applications - such as data mining, web searching,multimedia data retrieval, face recognition, and cursivehandwriting recognition - require robust and efficient patternrecognition techniques. Statistical decision making and estimationare regarded as fundamental to the study of pattern recognition. Statistical Pattern Recognition, Second Edition has been fullyupdated with new methods, applications and references. It providesa comprehensive introduction to this vibrant area - with materialdrawn from engineering, statistics, computer science and the socialsciences - and covers many application areas, such as databasedesign, artificial neural networks, and decision supportsystems. * Provides a self-contained introduction to statistical patternrecognition. * Each technique described is illustrated by real examples. * Covers Bayesian methods, neural networks, support vectormachines, and unsupervised classification. * Each section concludes with a description of the applicationsthat have been addressed and with further developments of thetheory. * Includes background material on dissimilarity, parameterestimation, data, linear algebra and probability. * Features a variety of exercises, from 'open-book' questions tomore lengthy projects. The book is aimed primarily at senior undergraduate and graduatestudents studying statistical pattern recognition, patternprocessing, neural networks, and data mining, in both statisticsand engineering departments. It is also an excellent source ofreference for technical professionals working in advancedinformation development environments. For further information on the techniques and applicationsdiscussed in this book please visit ahref="http://www.statistical-pattern-recognition.net/"www.statistical-pattern-recognition.net/a

A Statistical Approach to Neural Networks for Pattern Recognition

Robert A. Dunne
A Statistical Approach to Neural Networks for Pattern Recognition Book PDF

Author : Robert A. Dunne
Publisher : John Wiley & Sons
Page : 289 pages
File Size : 50,9 Mb
Release : 2007-07-20
Category : Mathematics
ISBN : 9780470148143

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A Statistical Approach to Neural Networks for Pattern Recognition by Robert A. Dunne Pdf

An accessible and up-to-date treatment featuring the connection between neural networks and statistics A Statistical Approach to Neural Networks for Pattern Recognition presents a statistical treatment of the Multilayer Perceptron (MLP), which is the most widely used of the neural network models. This book aims to answer questions that arise when statisticians are first confronted with this type of model, such as: How robust is the model to outliers? Could the model be made more robust? Which points will have a high leverage? What are good starting values for the fitting algorithm? Thorough answers to these questions and many more are included, as well as worked examples and selected problems for the reader. Discussions on the use of MLP models with spatial and spectral data are also included. Further treatment of highly important principal aspects of the MLP are provided, such as the robustness of the model in the event of outlying or atypical data; the influence and sensitivity curves of the MLP; why the MLP is a fairly robust model; and modifications to make the MLP more robust. The author also provides clarification of several misconceptions that are prevalent in existing neural network literature. Throughout the book, the MLP model is extended in several directions to show that a statistical modeling approach can make valuable contributions, and further exploration for fitting MLP models is made possible via the R and S-PLUS® codes that are available on the book's related Web site. 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.

From Statistics to Neural Networks

Vladimir Cherkassky,Jerome H. Friedman,Harry Wechsler
From Statistics to Neural Networks Book PDF

Author : Vladimir Cherkassky,Jerome H. Friedman,Harry Wechsler
Publisher : Springer Science & Business Media
Page : 414 pages
File Size : 47,5 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9783642791192

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From Statistics to Neural Networks by Vladimir Cherkassky,Jerome H. Friedman,Harry Wechsler Pdf

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.

Neural Networks In Pattern Recognition And Their Applications

Chi Hau Chen
Neural Networks In Pattern Recognition And Their Applications Book PDF

Author : Chi Hau Chen
Publisher : World Scientific
Page : 176 pages
File Size : 41,5 Mb
Release : 1991-12-27
Category : Computers
ISBN : 9789814505994

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Neural Networks In Pattern Recognition And Their Applications by Chi Hau Chen Pdf

The revitalization of neural network research in the past few years has already had a great impact on research and development in pattern recognition and artificial intelligence. Although neural network functions are not limited to pattern recognition, there is no doubt that a renewed progress in pattern recognition and its applications now critically depends on neural networks. This volume specially brings together outstanding original research papers in the area and aims to help the continued progress in pattern recognition and its applications.

Artificial Neural Networks in Pattern Recognition

Neamat El Gayar,Friedhelm Schwenker,Cheng Suen
Artificial Neural Networks in Pattern Recognition Book PDF

Author : Neamat El Gayar,Friedhelm Schwenker,Cheng Suen
Publisher : Springer
Page : 289 pages
File Size : 46,6 Mb
Release : 2014-09-29
Category : Computers
ISBN : 9783319116563

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Artificial Neural Networks in Pattern Recognition by Neamat El Gayar,Friedhelm Schwenker,Cheng Suen Pdf

This book constitutes the refereed proceedings of the 6th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2014, held in Montreal, QC, Canada, in October 2014. The 24 revised full papers presented were carefully reviewed and selected from 37 submissions for inclusion in this volume. They cover a large range of topics in the field of learning algorithms and architectures and discussing the latest research, results, and ideas in these areas.

Pattern Classification

Jgen Schmann
Pattern Classification Book PDF

Author : Jgen Schmann
Publisher : Wiley-Interscience
Page : 424 pages
File Size : 50,8 Mb
Release : 1996-03-15
Category : Business & Economics
ISBN : UOM:39015037276188

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Pattern Classification by Jgen Schmann Pdf

PATTERN CLASSIFICATION a unified view of statistical and neural approaches The product of years of research and practical experience in pattern classification, this book offers a theory-based engineering perspective on neural networks and statistical pattern classification. Pattern Classification sheds new light on the relationship between seemingly unrelated approaches to pattern recognition, including statistical methods, polynomial regression, multilayer perceptron, and radial basis functions. Important topics such as feature selection, reject criteria, classifier performance measurement, and classifier combinations are fully covered, as well as material on techniques that, until now, would have required an extensive literature search to locate. A full program of illustrations, graphs, and examples helps make the operations and general properties of different classification approaches intuitively understandable. Offering a lucid presentation of complex applications and their algorithms, Pattern Classification is an invaluable resource for researchers, engineers, and graduate students in this rapidly developing field.

Neural Networks for Pattern Recognition

Albert Nigrin
Neural Networks for Pattern Recognition Book PDF

Author : Albert Nigrin
Publisher : MIT Press
Page : 450 pages
File Size : 52,9 Mb
Release : 1993
Category : Computers
ISBN : 0262140543

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Neural Networks for Pattern Recognition by Albert Nigrin Pdf

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.

Artificial Neural Networks in Pattern Recognition

Lionel Prevost,Simone Marinai,Friedhelm Schwenker
Artificial Neural Networks in Pattern Recognition Book PDF

Author : Lionel Prevost,Simone Marinai,Friedhelm Schwenker
Publisher : Springer
Page : 322 pages
File Size : 40,9 Mb
Release : 2008-06-30
Category : Computers
ISBN : 9783540699392

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Artificial Neural Networks in Pattern Recognition by Lionel Prevost,Simone Marinai,Friedhelm Schwenker Pdf

Annotation 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.

Artificial Neural Networks in Pattern Recognition

Luca Pancioni,Friedhelm Schwenker,Edmondo Trentin
Artificial Neural Networks in Pattern Recognition Book PDF

Author : Luca Pancioni,Friedhelm Schwenker,Edmondo Trentin
Publisher : Springer
Page : 415 pages
File Size : 40,6 Mb
Release : 2018-08-29
Category : Computers
ISBN : 9783319999784

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Artificial Neural Networks in Pattern Recognition by Luca Pancioni,Friedhelm Schwenker,Edmondo Trentin Pdf

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 and Statistical Learning

Ke-Lin Du,M. N. S. Swamy
Neural Networks and Statistical Learning Book PDF

Author : Ke-Lin Du,M. N. S. Swamy
Publisher : Springer Science & Business Media
Page : 834 pages
File Size : 48,8 Mb
Release : 2013-12-09
Category : Technology & Engineering
ISBN : 9781447155713

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Neural Networks and Statistical Learning by Ke-Lin Du,M. N. S. Swamy Pdf

Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardware implementations, and some machine learning topics. Applications to biometric/bioinformatics and data mining are also included. Focusing on the prominent accomplishments and their practical aspects, academic and technical staff, graduate students and researchers will find that this provides a solid foundation and encompassing reference for the fields of neural networks, pattern recognition, signal processing, machine learning, computational intelligence, and data mining.

Artificial Neural Networks in Pattern Recognition

Frank-Peter Schilling,Thilo Stadelmann
Artificial Neural Networks in Pattern Recognition Book PDF

Author : Frank-Peter Schilling,Thilo Stadelmann
Publisher : Springer Nature
Page : 313 pages
File Size : 47,7 Mb
Release : 2020-09-01
Category : Computers
ISBN : 9783030583095

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Artificial Neural Networks in Pattern Recognition by Frank-Peter Schilling,Thilo Stadelmann Pdf

This book constitutes the refereed proceedings of the 9th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2020, held in Winterthur, Switzerland, in September 2020. The conference was held virtually due to the COVID-19 pandemic. The 22 revised full papers presented were carefully reviewed and selected from 34 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.

Statistical and Neural Classifiers

Sarunas Raudys
Statistical and Neural Classifiers Book PDF

Author : Sarunas Raudys
Publisher : Springer Science & Business Media
Page : 309 pages
File Size : 43,5 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9781447103592

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Statistical and Neural Classifiers by Sarunas Raudys Pdf

The classification of patterns is an important area of research which is central to all pattern recognition fields, including speech, image, robotics, and data analysis. Neural networks have been used successfully in a number of these fields, but so far their application has been based on a 'black box approach' with no real understanding of how they work. In this book, Sarunas Raudys - an internationally respected researcher in the area - provides an excellent mathematical and applied introduction to how neural network classifiers work and how they should be used.. .