Neural Networks for Pattern Recognition

Author: Christopher M. Bishop

Publisher: Oxford University Press

ISBN: 0198538642

Category: Computers

Page: 482

View: 2641

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

Neural Network Learning

Theoretical Foundations

Author: Martin Anthony,Peter L. Bartlett

Publisher: Cambridge University Press

ISBN: 9780521118620

Category: Computers

Page: 389

View: 4493

This book 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. The authors also discuss the computational complexity of neural network learning, describing a variety of hardness results, and outlining two efficient constructive learning algorithms. The book is essentially self-contained, since it introduces the necessary background material on probability, statistics, combinatorics and computational complexity; and it is intended to be accessible to researchers and graduate students in computer science, engineering, and mathematics.

Pattern Recognition and Neural Networks

Author: Brian D. Ripley

Publisher: Cambridge University Press

ISBN: 9780521717700

Category: Computers

Page: 403

View: 7332

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.


Algorithms for Pattern Recognition

Author: Ian Nabney

Publisher: Springer Science & Business Media

ISBN: 9781852334406

Category: Computers

Page: 420

View: 4059

This volume provides students, researchers and application developers with the knowledge and tools to get the most out of using neural networks and related data modelling techniques to solve pattern recognition problems. Each chapter covers a group of related pattern recognition techniques and includes a range of examples to show how these techniques can be applied to solve practical problems. Features of particular interest include: - A NETLAB toolbox which is freely available - Worked examples, demonstration programs and over 100 graded exercises - Cutting edge research made accessible for the first time in a highly usable form - Comprehensive coverage of visualisation methods, Bayesian techniques for neural networks and Gaussian Processes 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. "...provides a unique collection of many of the most important pattern recognition algorithms. With its use of compact and easily modified MATLAB scripts, the book is ideally suited to both teaching and research." Christopher Bishop, Microsoft Research, Cambridge, UK "...a welcome addition to the literature on neural networks and how to train and use them to solve many of the statistical problems that occur in data analysis and data mining" Jack Cowan, Mathematics Department, University of Chicago, US "If you have a pattern recognition problem, you should consider NETLAB; if you use NETLAB you must have this book." Keith Worden, University of Sheffield, UK

An Introduction to Neural Networks

Author: Kevin Gurney

Publisher: CRC Press

ISBN: 1482286998

Category: Computers

Page: 234

View: 4260

Though mathematical ideas underpin the study of neural networks, the author presents the fundamentals without the full mathematical apparatus. All aspects of the field are tackled, including artificial neurons as models of their real counterparts; the geometry of network action in pattern space; gradient descent methods, including back-propagation; associative memory and Hopfield nets; and self-organization and feature maps. The traditionally difficult topic of adaptive resonance theory is clarified within a hierarchical description of its operation. The book also includes several real-world examples to provide a concrete focus. This should enhance its appeal to those involved in the design, construction and management of networks in commercial environments and who wish to improve their understanding of network simulator packages. As a comprehensive and highly accessible introduction to one of the most important topics in cognitive and computer science, this volume should interest a wide range of readers, both students and professionals, in cognitive science, psychology, computer science and electrical engineering.

Pattern Recognition and Machine Learning

Author: Christopher M. Bishop

Publisher: Springer

ISBN: 9781493938438

Category: Computers

Page: 738

View: 8910

This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

Neural Networks and Machine Learning

Author: Christopher Bishop

Publisher: Springer

ISBN: 9783540649281

Category: Computers

Page: 353

View: 5201

In recent years neural computing has emerged as a practical technology, with successful applications in many fields. The majority of these applications are concerned with problems in pattern recognition, and make use of feedforward network architectures such as the multilayer perceptron and the radial basis function network. Also, it has become widely acknowledged that successful applications of neural computing require a principled, rather than ad hoc, approach. (From the preface to "Neural Networks for Pattern Recognition" by C.M. Bishop, Oxford Univ Press 1995.) This NATO volume, based on a 1997 workshop, presents a coordinated series of tutorial articles covering recent developments in the field of neural computing. It is ideally suited to graduate students and researchers.

Neural Networks and Statistical Learning

Author: Ke-Lin Du,M. N. S. Swamy

Publisher: Springer Science & Business Media

ISBN: 1447155718

Category: Computers

Page: 824

View: 1692

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.
Business & Economics

Neural Networks in Finance

Gaining Predictive Edge in the Market

Author: Paul D. McNelis

Publisher: Academic Press

ISBN: 0124859674

Category: Business & Economics

Page: 243

View: 1831

This book explores the intuitive appeal of neural networks and the genetic algorithm in finance. It demonstrates how neural networks used in combination with evolutionary computation outperform classical econometric methods for accuracy in forecasting, classification and dimensionality reduction. McNelis utilizes a variety of examples, from forecasting automobile production and corporate bond spread, to inflation and deflation processes in Hong Kong and Japan, to credit card default in Germany to bank failures in Texas, to cap-floor volatilities in New York and Hong Kong. * Offers a balanced, critical review of the neural network methods and genetic algorithms used in finance * Includes numerous examples and applications * Numerical illustrations use MATLAB code and the book is accompanied by a website

Unsupervised Learning

Foundations of Neural Computation

Author: Geoffrey E. Hinton,Terrence Joseph Sejnowski

Publisher: MIT Press

ISBN: 9780262581684

Category: Computers

Page: 398

View: 9839

Introduction; Unsupervised learning; Local synaptic learning rules suffice to maximize mutual information in a linear network; Convergent algorithm for sensory receptive field development; Emergence of position-independent detectors of snese of rotation and dilation with hebbian learning: an analysis; Learning invariance from transformation sequences; Learning perceptually salient visual parameters using spatiotemporal smoothness constraints; Wht is the goal of sensory coding?; An information-maximization approach to blind separation and blid deconvolution; Natural gradient works efficiently in learning; A fast fixed-point algorithm for independent component analysis; Feature extraction using an unsupervised neural network; Learning mixture models of spatial coherence; Baynesian self-organization driven byprior probability distributions; Finding minimum entropy codes; Learning population codes by minimizing description lengththe Helmholtz machine; factor analysis using delta-rule wake-sleep learning; Dimension reduction by local principal component analysis; A resource-allocating network for function interpolation; 20. Learning with preknowledge: clustering with point and graph matching distance measures; 21. Learning to generalize from single examples in the dynamic ling architecture; Index.
Signal processing

Signal Processing for Communications

Author: Paolo Prandoni,Martin Vetterli

Publisher: Collection le savoir suisse

ISBN: 2940222207

Category: Signal processing

Page: 394

View: 4444

With a novel, less classical approach to the subject, the authors have written a book with the conviction that signal processing should be taught to be fun. The treatment is therefore less focused on the mathematics and more on the conceptual aspects, the idea being to allow the readers to think about the subject at a higher conceptual level, thus building the foundations for more advanced topics. The book remains an engineering text, with the goal of helping students solve real-world problems. In this vein, the last chapter pulls together the individual topics as discussed throughout the book into an in-depth look at the development of an end-to-end communication system, namely, a modem for communicating digital information over an analog channel.

Markov Chain Monte Carlo in Practice

Author: W.R. Gilks,S. Richardson,David Spiegelhalter

Publisher: CRC Press

ISBN: 9780412055515

Category: Mathematics

Page: 512

View: 1290

In a family study of breast cancer, epidemiologists in Southern California increase the power for detecting a gene-environment interaction. In Gambia, a study helps a vaccination program reduce the incidence of Hepatitis B carriage. Archaeologists in Austria place a Bronze Age site in its true temporal location on the calendar scale. And in France, researchers map a rare disease with relatively little variation. Each of these studies applied Markov chain Monte Carlo methods to produce more accurate and inclusive results. General state-space Markov chain theory has seen several developments that have made it both more accessible and more powerful to the general statistician. Markov Chain Monte Carlo in Practice introduces MCMC methods and their applications, providing some theoretical background as well. The authors are researchers who have made key contributions in the recent development of MCMC methodology and its application. Considering the broad audience, the editors emphasize practice rather than theory, keeping the technical content to a minimum. The examples range from the simplest application, Gibbs sampling, to more complex applications. The first chapter contains enough information to allow the reader to start applying MCMC in a basic way. The following chapters cover main issues, important concepts and results, techniques for implementing MCMC, improving its performance, assessing model adequacy, choosing between models, and applications and their domains. Markov Chain Monte Carlo in Practice is a thorough, clear introduction to the methodology and applications of this simple idea with enormous potential. It shows the importance of MCMC in real applications, such as archaeology, astronomy, biostatistics, genetics, epidemiology, and image analysis, and provides an excellent base for MCMC to be applied to other fields as well.

Parallel Models of Associative Memory

Updated Edition

Author: Geoffrey E. Hinton,James A. Anderson

Publisher: Psychology Press

ISBN: 1317785207

Category: Psychology

Page: 352

View: 4361

This update of the 1981 classic on neural networks includes new commentaries by the authors that show how the original ideas are related to subsequent developments. As researchers continue to uncover ways of applying the complex information processing abilities of neural networks, they give these models an exciting future which may well involve revolutionary developments in understanding the brain and the mind -- developments that may allow researchers to build adaptive intelligent machines. The original chapters show where the ideas came from and the new commentaries show where they are going.

Probabilistic Networks and Expert Systems

Exact Computational Methods for Bayesian Networks

Author: Robert G. Cowell,Philip Dawid,Steffen L. Lauritzen,David J. Spiegelhalter

Publisher: Springer Science & Business Media

ISBN: 9780387718231

Category: Computers

Page: 324

View: 382

Winner of the 2002 DeGroot Prize. Probabilistic expert systems are graphical networks that support the modelling of uncertainty and decisions in large complex domains, while retaining ease of calculation. Building on original research by the authors over a number of years, this book gives a thorough and rigorous mathematical treatment of the underlying ideas, structures, and algorithms, emphasizing those cases in which exact answers are obtainable. It covers both the updating of probabilistic uncertainty in the light of new evidence, and statistical inference, about unknown probabilities or unknown model structure, in the light of new data. The careful attention to detail will make this work an important reference source for all those involved in the theory and applications of probabilistic expert systems. This book was awarded the first DeGroot Prize by the International Society for Bayesian Analysis for a book making an important, timely, thorough, and notably original contribution to the statistics literature. Robert G. Cowell is a Lecturer in the Faculty of Actuarial Science and Insurance of the Sir John Cass Business School, City of London. He has been working on probabilistic expert systems since 1989. A. Philip Dawid is Professor of Statistics at Cambridge University. He has served as Editor of the Journal of the Royal Statistical Society (Series B), Biometrika and Bayesian Analysis, and as President of the International Society for Bayesian Analysis. He holds the Royal Statistical Society Guy Medal in Bronze and in Silver, and the Snedecor Award for the Best Publication in Biometry. Steffen L. Lauritzen is Professor of Statistics at the University of Oxford. He has served as Editor of the Scandinavian Journal of Statistics. He holds the Royal Statistical Society Guy Medal in Silver and is an Honorary Fellow of the same society. He has, jointly with David J. Spiegelhalter, received the American Statistical Association’s award for an "Outstanding Statistical Application." David J. Spiegelhalter is Winton Professor of the Public Understanding of Risk at Cambridge University and Senior Scientist in the MRC Biostatistics Unit, Cambridge. He has published extensively on Bayesian methodology and applications, and holds the Royal Statistical Society Guy Medal in Bronze and in Silver.

Bayesian Reasoning and Machine Learning

Author: David Barber

Publisher: Cambridge University Press

ISBN: 0521518148

Category: Computers

Page: 697

View: 3917

A practical introduction perfect for final-year undergraduate and graduate students without a solid background in linear algebra and calculus.

Learning with Kernels

Support Vector Machines, Regularization, Optimization, and Beyond

Author: Bernhard Schölkopf,Alexander J. Smola

Publisher: MIT Press

ISBN: 9780262194754

Category: Computers

Page: 626

View: 5110

A comprehensive introduction to Support Vector Machines and related kernel methods.

Pulsed Neural Networks

Author: Wolfgang Maass,Christopher M. Bishop

Publisher: MIT Press

ISBN: 9780262632218

Category: Computers

Page: 377

View: 4146

Most practical applications of artificial neural networks are based on acomputational model involving the propagation of continuous variables from one processing unit tothe next. In recent years, data from neurobiological experiments have made it increasingly clearthat biological neural networks, which communicate through pulses, use the timing of the pulses totransmit information and perform computation. This realization has stimulated significant researchon pulsed neural networks, including theoretical analyses and model development, neurobiologicalmodeling, and hardware implementation. This book presents the complete spectrum ofcurrent research in pulsed neural networks and includes the most important work from many of the keyscientists in the field. Terrence J. Sejnowski's foreword, "Neural Pulse Coding," presents anoverview of the topic. The first half of the book consists of longer tutorial articles spanningneurobiology, theory, algorithms, and hardware. The second half contains a larger number of shorterresearch chapters that present more advanced concepts. The contributors use consistent notation andterminology throughout the book. Contributors: Peter S. Burge, Stephen R. Deiss,Rodney J. Douglas, John G. Elias, Wulfram Gerstner, Alister Hamilton, David Horn, Axel Jahnke,Richard Kempter, Wolfgang Maass, Alessandro Mortara, Alan F. Murray, David P. M. Northmore, IritOpher, Kostas A. Papathanasiou, Michael Recce, Barry J. P. Rising, Ulrich Roth, Tim Schönauer,Terrence J. Sejnowski, John Shawe-Taylor, Max R. van Daalen, J. Leo van Hemmen, Philippe Venier,Hermann Wagner, Adrian M. Whatley, Anthony M. Zador.

Fundamentals of Neural Networks

Architectures, Algorithms, and Applications

Author: Laurene V. Fausett,Laurene Fausett

Publisher: Prentice Hall

ISBN: 9780133341867

Category: Computers

Page: 461

View: 3776

Providing detailed examples of simple applications, this new book introduces the use of neural networks. It covers simple neural nets for pattern classification; pattern association; neural networks based on competition; adaptive-resonance theory; and more. For professionals working with neural networks.

Handbook of Statistical Analysis and Data Mining Applications

Author: Robert Nisbet,Gary Miner,Ken Yale

Publisher: Elsevier

ISBN: 0124166458

Category: Mathematics

Page: 822

View: 6288

Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application. This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas—from science and engineering, to medicine, academia and commerce. Includes input by practitioners for practitioners Includes tutorials in numerous fields of study that provide step-by-step instruction on how to use supplied tools to build models Contains practical advice from successful real-world implementations Brings together, in a single resource, all the information a beginner needs to understand the tools and issues in data mining to build successful data mining solutions Features clear, intuitive explanations of novel analytical tools and techniques, and their practical applications