Computers

Pattern classification

Author: Richard O. Duda,Peter E. Hart,David G. Stork

Publisher: Wiley-Interscience

ISBN: N.A

Category: Computers

Page: 654

View: 7510

This edition has been completely revised, enlarged and formatted in two colour. It is a systematic account of the major topics in pattern recognition, based on the fundamental principles. It includes extensive examples, exercises and a solutions manual.
Technology & Engineering

Combining Pattern Classifiers

Methods and Algorithms

Author: Ludmila I. Kuncheva

Publisher: John Wiley & Sons

ISBN: 1118914546

Category: Technology & Engineering

Page: 384

View: 4638

A unified, coherent treatment of current classifier ensemble methods, from fundamentals of pattern recognition to ensemble feature selection, now in its second edition The art and science of combining pattern classifiers has flourished into a prolific discipline since the first edition of Combining Pattern Classifiers was published in 2004. Dr. Kuncheva has plucked from the rich landscape of recent classifier ensemble literature the topics, methods, and algorithms that will guide the reader toward a deeper understanding of the fundamentals, design, and applications of classifier ensemble methods. Thoroughly updated, with MATLAB® code and practice data sets throughout, Combining Pattern Classifiers includes: Coverage of Bayes decision theory and experimental comparison of classifiers Essential ensemble methods such as Bagging, Random forest, AdaBoost, Random subspace, Rotation forest, Random oracle, and Error Correcting Output Code, among others Chapters on classifier selection, diversity, and ensemble feature selection With firm grounding in the fundamentals of pattern recognition, and featuring more than 140 illustrations, Combining Pattern Classifiers, Second Edition is a valuable reference for postgraduate students, researchers, and practitioners in computing and engineering.
Mathematics

Statistical Pattern Recognition

Author: Andrew R. Webb,Keith D. Copsey

Publisher: John Wiley & Sons

ISBN: 1119961408

Category: Mathematics

Page: 616

View: 9726

Statistical pattern recognition relates to the use of statistical techniques for analysing data measurements in order to extract information and make justified decisions. It is a very active area of study and research, which has seen many advances in recent years. Applications such as data mining, web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition, all require robust and efficient pattern recognition techniques. This third edition provides an introduction to statistical pattern theory and techniques, with material drawn from a wide range of fields, including the areas of engineering, statistics, computer science and the social sciences. The book has been updated to cover new methods and applications, and includes a wide range of techniques such as Bayesian methods, neural networks, support vector machines, feature selection and feature reduction techniques.Technical descriptions and motivations are provided, and the techniques are illustrated using real examples. Statistical Pattern Recognition, 3rd Edition: Provides a self-contained introduction to statistical pattern recognition. Includes new material presenting the analysis of complex networks. Introduces readers to methods for Bayesian density estimation. Presents descriptions of new applications in biometrics, security, finance and condition monitoring. Provides descriptions and guidance for implementing techniques, which will be invaluable to software engineers and developers seeking to develop real applications Describes mathematically the range of statistical pattern recognition techniques. Presents a variety of exercises including more extensive computer projects. The in-depth technical descriptions make the book suitable for senior undergraduate and graduate students in statistics, computer science and engineering. Statistical Pattern Recognition is also an excellent reference source for technical professionals. Chapters have been arranged to facilitate implementation of the techniques by software engineers and developers in non-statistical engineering fields. www.wiley.com/go/statistical_pattern_recognition
Computers

Robot Vision

Second International Workshop, RobVis 2008, Auckland, New Zealand, February 18-20, 2008, Proceedings

Author: Gerald Sommer,Reinhard Klette

Publisher: Springer Science & Business Media

ISBN: 3540781560

Category: Computers

Page: 468

View: 4501

This book constitutes the refereed proceedings of the Second International Workshop on Robot Vision, RobVis 2008, held in Auckland, New Zealand, in February 2008. The 21 revised full papers presented together with 15 posters papers were carefully reviewed and selected from 59 submissions. The papers and posters are organized in topical sections on motion analysis, stereo vision, robot vision, computer vision, visual inspection, urban vision, and the poster section.
Computers

Evolutionary Synthesis of Pattern Recognition Systems

Author: Bir Bhanu,Lin Yingqiang,Krzysztof Krawiec

Publisher: Springer Science & Business Media

ISBN: 9780387212951

Category: Computers

Page: 296

View: 2212

Integrates computer vision, pattern recognition, and AI. Presents original research that will benefit researchers and professionals in computer vision, pattern recognition, target recognition, machine learning, evolutionary learning, image processing, knowledge discovery and data mining, cybernetics, robotics, automation and psychology
Computers

Distributed Sensor Networks, Second Edition

Image and Sensor Signal Processing

Author: S. Sitharama Iyengar,Richard R. Brooks

Publisher: CRC Press

ISBN: 1439862826

Category: Computers

Page: 764

View: 6158

The best-selling Distributed Sensor Networks became the definitive guide to understanding this far-reaching technology. Preserving the excellence and accessibility of its predecessor, Distributed Sensor Networks, Second Edition once again provides all the fundamentals and applications in one complete, self-contained source. Ideal as a tutorial for students or as research material for engineers, the book gives readers up-to-date, practical insight on all aspects of the field. Revised and expanded, this second edition incorporates contributions from many veterans of the DARPA ISO SENSIT program as well as new material from distinguished researchers in the field. Image and Sensor Signal Processing focuses on software issues and the history and future of sensor networks. The book also covers information fusion and power management. Readers of this book may also be interested in Distributed Sensor Networks, Second Edition: Sensor Networking and Applications (ISBN: 9781439862872).
Mathematics

Graphs for Pattern Recognition

Infeasible Systems of Linear Inequalities

Author: Damir Gainanov

Publisher: Walter de Gruyter GmbH & Co KG

ISBN: 3110481065

Category: Mathematics

Page: 158

View: 6384

Data mining and pattern recognition are areas based on the mathematical constructions discussed in this monograph. By using combinatorial and graph theoretical techniques, it is shown how to tackle infeasible systems of linear inequalities. These are, in turn, building blocks of geometric decision rules for pattern recognition.
Computers

Data Mining

Concepts, Models, Methods, and Algorithms

Author: Mehmed Kantardzic

Publisher: John Wiley & Sons

ISBN: 0470890452

Category: Computers

Page: 480

View: 2388

This book reviews state-of-the-art methodologies and techniques for analyzing enormous quantities of raw data in high-dimensional data spaces, to extract new information for decision making. The goal of this book is to provide a single introductory source, organized in a systematic way, in which we could direct the readers in analysis of large data sets, through the explanation of basic concepts, models and methodologies developed in recent decades.
Mathematics

Methods of Multivariate Analysis

Author: Alvin C. Rencher,William F. Christensen

Publisher: John Wiley & Sons

ISBN: 1118391675

Category: Mathematics

Page: 800

View: 3222

Praise for the Second Edition "This book is a systematic, well-written, well-organized text on multivariate analysis packed with intuition and insight . . . There is much practical wisdom in this book that is hard to find elsewhere." —IIE Transactions Filled with new and timely content, Methods of Multivariate Analysis, Third Edition provides examples and exercises based on more than sixty real data sets from a wide variety of scientific fields. It takes a "methods" approach to the subject, placing an emphasis on how students and practitioners can employ multivariate analysis in real-life situations. This Third Edition continues to explore the key descriptive and inferential procedures that result from multivariate analysis. Following a brief overview of the topic, the book goes on to review the fundamentals of matrix algebra, sampling from multivariate populations, and the extension of common univariate statistical procedures (including t-tests, analysis of variance, and multiple regression) to analogous multivariate techniques that involve several dependent variables. The latter half of the book describes statistical tools that are uniquely multivariate in nature, including procedures for discriminating among groups, characterizing low-dimensional latent structure in high-dimensional data, identifying clusters in data, and graphically illustrating relationships in low-dimensional space. In addition, the authors explore a wealth of newly added topics, including: Confirmatory Factor Analysis Classification Trees Dynamic Graphics Transformations to Normality Prediction for Multivariate Multiple Regression Kronecker Products and Vec Notation New exercises have been added throughout the book, allowing readers to test their comprehension of the presented material. Detailed appendices provide partial solutions as well as supplemental tables, and an accompanying FTP site features the book's data sets and related SAS® code. Requiring only a basic background in statistics, Methods of Multivariate Analysis, Third Edition is an excellent book for courses on multivariate analysis and applied statistics at the upper-undergraduate and graduate levels. The book also serves as a valuable reference for both statisticians and researchers across a wide variety of disciplines.
Mathematics

Statistical Analysis with Missing Data

Author: Roderick J. A. Little,Donald B. Rubin

Publisher: John Wiley & Sons

ISBN: 1118625889

Category: Mathematics

Page: 408

View: 1247

Praise for the First Edition of Statistical Analysis with Missing Data "An important contribution to the applied statistics literature.... I give the book high marks for unifying and making accessible much of the past and current work in this important area." —William E. Strawderman, Rutgers University "This book...provide[s] interesting real-life examples, stimulating end-of-chapter exercises, and up-to-date references. It should be on every applied statistician’s bookshelf." —The Statistician "The book should be studied in the statistical methods department in every statistical agency." —Journal of Official Statistics Statistical analysis of data sets with missing values is a pervasive problem for which standard methods are of limited value. The first edition of Statistical Analysis with Missing Data has been a standard reference on missing-data methods. Now, reflecting extensive developments in Bayesian methods for simulating posterior distributions, this Second Edition by two acknowledged experts on the subject offers a thoroughly up-to-date, reorganized survey of current methodology for handling missing-data problems. Blending theory and application, authors Roderick Little and Donald Rubin review historical approaches to the subject and describe rigorous yet simple methods for multivariate analysis with missing values. They then provide a coherent theory for analysis of problems based on likelihoods derived from statistical models for the data and the missing-data mechanism and apply the theory to a wide range of important missing-data problems. The new edition now enlarges its coverage to include: Expanded coverage of Bayesian methodology, both theoretical and computational, and of multiple imputation Analysis of data with missing values where inferences are based on likelihoods derived from formal statistical models for the data-generating and missing-data mechanisms Applications of the approach in a variety of contexts including regression, factor analysis, contingency table analysis, time series, and sample survey inference Extensive references, examples, and exercises Amstat News asked three review editors to rate their top five favorite books in the September 2003 issue. Statistical Analysis With Missing Data was among those chosen.
Business & Economics

The EM algorithm and extensions

Author: Geoffrey J. McLachlan,Thriyambakam Krishnan

Publisher: John Wiley & Sons

ISBN: N.A

Category: Business & Economics

Page: 359

View: 4650

The only single-source--now completely updated and revised--to offer a unified treatment of the theory, methodology, and applications of the EM algorithm Complete with updates that capture developments from the past decade, The EM Algorithm and Extensions, Second Edition successfully provides a basic understanding of the EM algorithm by describing its inception, implementation, and applicability in numerous statistical contexts. In conjunction with the fundamentals of the topic, the authors discuss convergence issues and computation of standard errors, and, in addition, unveil many parallels and connections between the EM algorithm and Markov chain Monte Carlo algorithms. Thorough discussions on the complexities and drawbacks that arise from the basic EM algorithm, such as slow convergence and lack of an in-built procedure to compute the covariance matrix of parameter estimates, are also presented. While the general philosophy of the First Edition has been maintained, this timely new edition has been updated, revised, and expanded to include: New chapters on Monte Carlo versions of the EM algorithm and generalizations of the EM algorithm New results on convergence, including convergence of the EM algorithm in constrained parameter spaces Expanded discussion of standard error computation methods, such as methods for categorical data and methods based on numerical differentiation Coverage of the interval EM, which locates all stationary points in a designated region of the parameter space Exploration of the EM algorithm's relationship with the Gibbs sampler and other Markov chain Monte Carlo methods Plentiful pedagogical elements-chapter introductions, lists of examples, author and subject indices, computer-drawn graphics, and a related Web site The EM Algorithm and Extensions, Second Edition serves as an excellent text for graduate-level statistics students and is also a comprehensive resource for theoreticians, practitioners, and researchers in the social and physical sciences who would like to extend their knowledge of the EM algorithm.
Science

Biomedical Signal Analysis

Author: Rangaraj M. Rangayyan

Publisher: John Wiley & Sons

ISBN: 1119068010

Category: Science

Page: 672

View: 3991

The book will help assist a reader in the development of techniques for analysis of biomedical signals and computer aided diagnoses with a pedagogical examination of basic and advanced topics accompanied by over 350 figures and illustrations. Wide range of filtering techniques presented to address various applications 800 mathematical expressions and equations Practical questions, problems and laboratory exercises Includes fractals and chaos theory with biomedical applications
Mathematics

Discriminant Analysis and Statistical Pattern Recognition

Author: Geoffrey McLachlan

Publisher: John Wiley & Sons

ISBN: 9780471691150

Category: Mathematics

Page: 526

View: 8180

The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "Survey Errors and Survey Costs is a well-written, well-presented, and highly readable text that should be on every error-conscious statistician?s bookshelf. Any courses that cover the theory and design of surveys should certainly have Survey Errors and Survey Costs on their reading lists." ?Phil Edwards MEL, Aston University Science Park, UK Review in The Statistician, Vol. 40, No. 3, 1991 "This volume is an extremely valuable contribution to survey methodology. It has many virtues: First, it provides a framework in which survey errors can be segregated by sources. Second, Groves has skillfully synthesized existing knowledge, bringing together in an easily accessible form empirical knowledge from a variety of sources. Third, he has managed to integrate into a common framework the contributions of several disciplines. For example, the work of psychometricians and cognitive psychologists is made relevant to the research of econometricians as well as the field experience of sociologists. Finally, but not least, Groves has managed to present all this in a style that is accessible to a wide variety of readers ranging from survey specialists to policymakers." ?Peter H. Rossi University of Massachusetts at Amherst Review in Journal of Official Statistics, January 1991
Science

Introduction to Computational Chemistry

Author: Frank Jensen

Publisher: John Wiley & Sons

ISBN: 1118825950

Category: Science

Page: 664

View: 6215

Introduction to Computational Chemistry 3rd Edition provides a comprehensive account of the fundamental principles underlying different computational methods. Fully revised and updated throughout to reflect important method developments and improvements since publication of the previous edition, this timely update includes the following significant revisions and new topics: * Polarizable force fields * Tight-binding DFT * More extensive DFT functionals, excited states and time dependent molecular properties * Accelerated Molecular Dynamics methods * Tensor decomposition methods * Cluster analysis * Reduced scaling and reduced prefactor methods Additional information is available at: www.wiley.com/go/jensen/computationalchemistry3
Science

Phylogenetics

The Theory and Practice of Phylogenetic Systematics

Author: E. O. Wiley,Ronald Ed. Wiley

Publisher: Wiley-Liss

ISBN: N.A

Category: Science

Page: 439

View: 6126

Presents a clear, simple and comprehensive overview of the phylogenetic approach to systematics, which has two major goals: reconstructing the evolutionary relationships among organisms and integrating the results into general reference classifications. Shows how the results of systematic research can be applied to studying the pattern and processes of evolution.
Science

Statistical Procedures for Agricultural Research

Author: Kwanchai A. Gomez,Arturo A. Gomez

Publisher: John Wiley & Sons

ISBN: 9780471870920

Category: Science

Page: 680

View: 4363

The Agricultural Field Experiment A Statistical Examination of Theory and Practice S.C. Pearce Here is a comprehensive overview of the statistical knowledge needed to conduct successful field experiments on crops. Blending theory and practice, it covers a wide range of subjects from the fertility of fields and the allocation of resources to the cooperative efforts needed for a successful experiment. 1983 (0 471-10511-2) 335 pp. Statistics for Experimenters An Introduction to Design, Data Analysis, and Model Building George E. P. Box, William G. Hunter and J. Stuart Hunter This fresh approach to statistics focuses on applications in the physical, engineering, biological, and social sciences. Written for the non-statistician, the book emphasizes the need for the investigator to make his research as effective as possible through the proper choice and conduct of experiments, and the appropriate analysis of data. Included are numerous worked examples, exercises with answers), and end-of-chapter questions and problems. 1978 (0 471-09315-7) 653 pp.
Computers

Machine Learning

An Algorithmic Perspective, Second Edition

Author: Stephen Marsland

Publisher: CRC Press

ISBN: 1498759785

Category: Computers

Page: 457

View: 6148

A Proven, Hands-On Approach for Students without a Strong Statistical Foundation Since the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer science students without a strong statistical background often find it hard to get started in this area. Remedying this deficiency, Machine Learning: An Algorithmic Perspective, Second Edition helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation. New to the Second Edition Two new chapters on deep belief networks and Gaussian processes Reorganization of the chapters to make a more natural flow of content Revision of the support vector machine material, including a simple implementation for experiments New material on random forests, the perceptron convergence theorem, accuracy methods, and conjugate gradient optimization for the multi-layer perceptron Additional discussions of the Kalman and particle filters Improved code, including better use of naming conventions in Python Suitable for both an introductory one-semester course and more advanced courses, the text strongly encourages students to practice with the code. Each chapter includes detailed examples along with further reading and problems. All of the code used to create the examples is available on the author’s website.
Computers

Data Warehousing Fundamentals

A Comprehensive Guide for IT Professionals

Author: Paulraj Ponniah

Publisher: John Wiley & Sons

ISBN: 0471463892

Category: Computers

Page: 544

View: 2427

Geared to IT professionals eager to get into the all-important field of data warehousing, this book explores all topics needed by those who design and implement data warehouses. Readers will learn about planning requirements, architecture, infrastructure, data preparation, information delivery, implementation, and maintenance. They'll also find a wealth of industry examples garnered from the author's 25 years of experience in designing and implementing databases and data warehouse applications for major corporations. Market: IT Professionals, Consultants.
Technology & Engineering

Speech and Audio Signal Processing

Processing and Perception of Speech and Music

Author: Ben Gold,Nelson Morgan,Dan Ellis

Publisher: John Wiley & Sons

ISBN: 1118142896

Category: Technology & Engineering

Page: 688

View: 2690

When Speech and Audio Signal Processing published in 1999, it stood out from its competition in its breadth of coverage and its accessible, intutiont-based style. This book was aimed at individual students and engineers excited about the broad span of audio processing and curious to understand the available techniques. Since then, with the advent of the iPod in 2001, the field of digital audio and music has exploded, leading to a much greater interest in the technical aspects of audio processing. This Second Edition will update and revise the original book to augment it with new material describing both the enabling technologies of digital music distribution (most significantly the MP3) and a range of exciting new research areas in automatic music content processing (such as automatic transcription, music similarity, etc.) that have emerged in the past five years, driven by the digital music revolution. New chapter topics include: Psychoacoustic Audio Coding, describing MP3 and related audio coding schemes based on psychoacoustic masking of quantization noise Music Transcription, including automatically deriving notes, beats, and chords from music signals. Music Information Retrieval, primarily focusing on audio-based genre classification, artist/style identification, and similarity estimation. Audio Source Separation, including multi-microphone beamforming, blind source separation, and the perception-inspired techniques usually referred to as Computational Auditory Scene Analysis (CASA).