Pattern Recognition

Author: Sergios Theodoridis,Konstantinos Koutroumbas

Publisher: Academic Press

ISBN: 9780080949123

Category: Computers

Page: 984

View: 2305

This book considers classical and current theory and practice, of supervised, unsupervised and semi-supervised pattern recognition, to build a complete background for professionals and students of engineering. The authors, leading experts in the field of pattern recognition, have provided an up-to-date, self-contained volume encapsulating this wide spectrum of information. The very latest methods are incorporated in this edition: semi-supervised learning, combining clustering algorithms, and relevance feedback. · Thoroughly developed to include many more worked examples to give greater understanding of the various methods and techniques · Many more diagrams included--now in two color--to provide greater insight through visual presentation · Matlab code of the most common methods are given at the end of each chapter. · More Matlab code is available, together with an accompanying manual, via this site · Latest hot topics included to further the reference value of the text including non-linear dimensionality reduction techniques, relevance feedback, semi-supervised learning, spectral clustering, combining clustering algorithms. · An accompanying book with Matlab code of the most common methods and algorithms in the book, together with a descriptive summary, and solved examples including real-life data sets in imaging, and audio recognition. The companion book will be available separately or at a special packaged price (ISBN: 9780123744869). Thoroughly developed to include many more worked examples to give greater understanding of the various methods and techniques Many more diagrams included--now in two color--to provide greater insight through visual presentation Matlab code of the most common methods are given at the end of each chapter An accompanying book with Matlab code of the most common methods and algorithms in the book, together with a descriptive summary and solved examples, and including real-life data sets in imaging and audio recognition. The companion book is available separately or at a special packaged price (Book ISBN: 9780123744869. Package ISBN: 9780123744913) Latest hot topics included to further the reference value of the text including non-linear dimensionality reduction techniques, relevance feedback, semi-supervised learning, spectral clustering, combining clustering algorithms Solutions manual, powerpoint slides, and additional resources are available to faculty using the text for their course. Register at www.textbooks.elsevier.com and search on "Theodoridis" to access resources for instructor.
Release

Introduction to Pattern Recognition

A Matlab Approach

Author: Sergios Theodoridis,Aggelos Pikrakis,Konstantinos Koutroumbas,Dionisis Cavouras

Publisher: Academic Press

ISBN: 9780080922751

Category: Computers

Page: 231

View: 9333

Introduction to Pattern Recognition: A Matlab Approach is an accompanying manual to Theodoridis/Koutroumbas' Pattern Recognition. It includes Matlab code of the most common methods and algorithms in the book, together with a descriptive summary and solved examples, and including real-life data sets in imaging and audio recognition. This text is designed for electronic engineering, computer science, computer engineering, biomedical engineering and applied mathematics students taking graduate courses on pattern recognition and machine learning as well as R&D engineers and university researchers in image and signal processing/analyisis, and computer vision. Matlab code and descriptive summary of the most common methods and algorithms in Theodoridis/Koutroumbas, Pattern Recognition, Fourth Edition Solved examples in Matlab, including real-life data sets in imaging and audio recognition Available separately or at a special package price with the main text (ISBN for package: 978-0-12-374491-3)
Release

Applied Pattern Recognition

Algorithms and Implementation in C++

Author: Dietrich W. R. Paulus,Joachim Hornegger

Publisher: Springer Science & Business Media

ISBN: 9783528355586

Category: Computers

Page: 372

View: 5370

This book demonstrates the efficiency of the C++ programming language in the realm of pattern recognition and pattern analysis. For this 4th edition, new features of the C++ language were integrated and their relevance for image and speech processing is discussed.
Release

Pattern Recognition

Author: William Gibson

Publisher: Penguin UK

ISBN: 0141904461

Category: Fiction

Page: 368

View: 7728

One of the most influential and imaginative writers of the past twenty years turns his attention to London - with dazzling results. Cayce Pollard owes her living to her pathological sensitivity to logos. In London to consult for the world's coolest ad agency, she finds herself catapulted, via her addiction to a mysterious body of fragmentary film footage, uploaded to the Web by a shadowy auteur, into a global quest for this unknown 'garage Kubrick'. Cayce becomes involved with an eccentric hacker, a vengeful ad executive, a defrocked mathematician, a Tokyo Otaku-coven known as Eye of the Dragon and, eventually, the elusive 'Kubrick' himself. William Gibson's new novel is about the eternal mystery of London, the coolest sneakers in the world, and life in (the former) USSR.
Release

Bayesian Programming

Author: Pierre Bessiere,Emmanuel Mazer,Juan Manuel Ahuactzin,Kamel Mekhnacha

Publisher: CRC Press

ISBN: 1439880336

Category: Business & Economics

Page: 380

View: 8364

Probability as an Alternative to Boolean Logic While logic is the mathematical foundation of rational reasoning and the fundamental principle of computing, it is restricted to problems where information is both complete and certain. However, many real-world problems, from financial investments to email filtering, are incomplete or uncertain in nature. Probability theory and Bayesian computing together provide an alternative framework to deal with incomplete and uncertain data. Decision-Making Tools and Methods for Incomplete and Uncertain Data Emphasizing probability as an alternative to Boolean logic, Bayesian Programming covers new methods to build probabilistic programs for real-world applications. Written by the team who designed and implemented an efficient probabilistic inference engine to interpret Bayesian programs, the book offers many Python examples that are also available on a supplementary website together with an interpreter that allows readers to experiment with this new approach to programming. Principles and Modeling Only requiring a basic foundation in mathematics, the first two parts of the book present a new methodology for building subjective probabilistic models. The authors introduce the principles of Bayesian programming and discuss good practices for probabilistic modeling. Numerous simple examples highlight the application of Bayesian modeling in different fields. Formalism and Algorithms The third part synthesizes existing work on Bayesian inference algorithms since an efficient Bayesian inference engine is needed to automate the probabilistic calculus in Bayesian programs. Many bibliographic references are included for readers who would like more details on the formalism of Bayesian programming, the main probabilistic models, general purpose algorithms for Bayesian inference, and learning problems. FAQs Along with a glossary, the fourth part contains answers to frequently asked questions. The authors compare Bayesian programming and possibility theories, discuss the computational complexity of Bayesian inference, cover the irreducibility of incompleteness, and address the subjectivist versus objectivist epistemology of probability. The First Steps toward a Bayesian Computer A new modeling methodology, new inference algorithms, new programming languages, and new hardware are all needed to create a complete Bayesian computing framework. Focusing on the methodology and algorithms, this book describes the first steps toward reaching that goal. It encourages readers to explore emerging areas, such as bio-inspired computing, and develop new programming languages and hardware architectures.
Release

Introduction to Statistical Pattern Recognition

Author: Keinosuke Fukunaga

Publisher: Elsevier

ISBN: 9780080478654

Category: Computers

Page: 592

View: 1909

This completely revised second edition presents an introduction to statistical pattern recognition. Pattern recognition in general covers a wide range of problems: it is applied to engineering problems, such as character readers and wave form analysis as well as to brain modeling in biology and psychology. Statistical decision and estimation, which are the main subjects of this book, are regarded as fundamental to the study of pattern recognition. This book is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field. Each chapter contains computer projects as well as exercises.
Release

Information Theory, Inference and Learning Algorithms

Author: David J. C. MacKay

Publisher: Cambridge University Press

ISBN: 9780521642989

Category: Computers

Page: 628

View: 1203

Fun and exciting textbook on the mathematics underpinning the most dynamic areas of modern science and engineering.
Release

Introduction to Machine Learning

Author: Ethem Alpaydin

Publisher: MIT Press

ISBN: 0262028182

Category: Computers

Page: 640

View: 9898

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. Subjects include supervised learning; Bayesian decision theory; parametric, semi-parametric, and nonparametric methods; multivariate analysis; hidden Markov models; reinforcement learning; kernel machines; graphical models; Bayesian estimation; and statistical testing.Machine learning is rapidly becoming a skill that computer science students must master before graduation. The third edition of Introduction to Machine Learning reflects this shift, with added support for beginners, including selected solutions for exercises and additional example data sets (with code available online). Other substantial changes include discussions of outlier detection; ranking algorithms for perceptrons and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesian methods. All learning algorithms are explained so that students can easily move from the equations in the book to a computer program. The book can be used by both advanced undergraduates and graduate students. It will also be of interest to professionals who are concerned with the application of machine learning methods.
Release

Machine Vision

Theory, Algorithms, Practicalities

Author: E. R. Davies

Publisher: Elsevier

ISBN: 1483275612

Category: Computers

Page: 572

View: 2072

Machine Vision: Theory, Algorithms, Practicalities covers the limitations, constraints, and tradeoffs of vision algorithms. This book is organized into four parts encompassing 21 chapters that tackle general topics, such as noise suppression, edge detection, principles of illumination, feature recognition, Bayes’ theory, and Hough transforms. Part 1 provides research ideas on imaging and image filtering operations, thresholding techniques, edge detection, and binary shape and boundary pattern analyses. Part 2 deals with the area of intermediate-level vision, the nature of the Hough transform, shape detection, and corner location. Part 3 demonstrates some of the practical applications of the basic work previously covered in the book. This part also discusses some of the principles underlying implementation, including on lighting and hardware systems. Part 4 highlights the limitations and constraints of vision algorithms and their corresponding solutions. This book will prove useful to students with undergraduate course on vision for electronic engineering or computer science.
Release

Pattern Recognition and Classification in Time Series Data

Author: Volna, Eva,Kotyrba, Martin,Janosek, Michal

Publisher: IGI Global

ISBN: 1522505660

Category: Computers

Page: 282

View: 9226

Patterns can be any number of items that occur repeatedly, whether in the behaviour of animals, humans, traffic, or even in the appearance of a design. As technologies continue to advance, recognizing, mimicking, and responding to all types of patterns becomes more precise. Pattern Recognition and Classification in Time Series Data focuses on intelligent methods and techniques for recognizing and storing dynamic patterns. Emphasizing topics related to artificial intelligence, pattern management, and algorithm development, in addition to practical examples and applications, this publication is an essential reference source for graduate students, researchers, and professionals in a variety of computer-related disciplines.
Release

Pattern Recognition and Machine Learning

Author: Christopher M. Bishop

Publisher: Springer

ISBN: 9781493938438

Category: Computers

Page: 738

View: 6158

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.
Release

Fetal Heart Rate Monitoring

Author: Freeman,Thomas J. Garite,Michael P. Nageotte,Lisa A. Miller

Publisher: Lippincott Williams & Wilkins

ISBN: 1451116632

Category: Medical

Page: 271

View: 4309

"Fetal heart rate monitoring is widely used by almost every obstetrician as a way to document the case and to help decrease health care costs. This is a short reference on the physiologic benefits, instrumentation, application and interpretation of fetalheart rate monitoring. The second half of the book uses actual FHR strips and cases to illustrate various anomalies (fetal distress, fetal distress in prematurity, fetus with CNS dysfunction). Several new drugs have been introduced for use during labor that effect FHR"--Provided by publisher.
Release

Gait Analysis

An Introduction

Author: Michael W. Whittle

Publisher: Butterworth-Heinemann

ISBN: 1483183734

Category: Medical

Page: 240

View: 6002

Gait Analysis: An Introduction focuses on the systematic study of human walking and its contributions in the medical management of diseases affecting the locomotor system. The book first covers normal gait and pathological gait. Discussions focus on common pathologies affecting gait, amputee gait, walking aids, particular gait abnormalities, gait in the elderly and the young, moments of force, energy consumption, gait cycle, muscular activity during gait, and optimization of energy usage. The manuscript then elaborates on the methods of gait analysis, including visual gait analysis, general gait parameters, timing the gait cycle, direct motion measurement systems, electrogoniometers, electromyography, accelerometers, gyroscopes, and force platforms. The publication tackles the applications of gait analysis, as well as clinical gait and scientific gait analysis, normal ranges for gait parameters, conversions between measurement units, and computer program for general gait parameters. The manuscript is a valuable source of data for students of physical therapy, bioengineering, orthopedics, rheumatology, neurology, and rehabilitation.
Release

Fuzzy Logic with Engineering Applications

Author: Timothy J. Ross

Publisher: John Wiley & Sons

ISBN: 0470748516

Category: Technology & Engineering

Page: 606

View: 5030

The first edition of Fuzzy Logic with Engineering Applications (1995) was the first classroom text for undergraduates in the field. Now updated for the second time, this new edition features the latest advances in the field including material on expansion of the MLFE method using genetic algorithms, cognitive mapping, fuzzy agent-based models and total uncertainty. Redundant or obsolete topics have been removed, resulting in a more concise yet inclusive text that will ensure the book retains its broad appeal at the forefront of the literature. Fuzzy Logic with Engineering Applications, 3rd Edition is oriented mainly towards methods and techniques. Every chapter has been revised, featuring new illustrations and examples throughout. Supporting MATLAB code is downloadable at www.wileyeurope.com/go/fuzzylogic. This will benefit student learning in all basic operations, the generation of membership functions, and the specialized applications in the latter chapters of the book, providing an invaluable tool for students as well as for self-study by practicing engineers.
Release

Data Mining: Concepts and Techniques

Author: Jiawei Han,Jian Pei,Micheline Kamber

Publisher: Elsevier

ISBN: 9780123814807

Category: Computers

Page: 744

View: 3767

Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining. This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining. Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data
Release

Neural Networks for Pattern Recognition

Author: Christopher M. Bishop

Publisher: Oxford University Press

ISBN: 0198538642

Category: Computers

Page: 482

View: 5218

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

Computer and Machine Vision

Theory, Algorithms, Practicalities

Author: E. R. Davies

Publisher: Academic Press

ISBN: 0123869080

Category: Technology & Engineering

Page: 871

View: 5499

Annotation. Computer and Machine Vision: Theory, Algorithms, Practicalities (previously entitled Machine Vision) clearly and systematically presents the basic methodology of computer and machine vision, covering the essential elements of the theory while emphasizing algorithmic and practical design constraints. This fully revised fourth edition has brought in more of the concepts and applications of computer vision, making it a very comprehensive and up-to-date tutorial text suitable for graduate students, researchers and R the first of these has been widely used internationally for more than 20 years, and is now out in this much enhanced fourth edition. Roy holds a DSc at the University of London, and has been awarded Distinguished Fellow of the British Machine Vision Association, and Fellow of the International Association of Pattern Recognition.Mathematics and essential theory are made approachable by careful explanations and well-illustrated examples.Updated content and new sections cover topics such as human iris location, image stitching, line detection using RANSAC, performance measures, and hyperspectral imaging.The 'recent developments' section now included in each chapter will be useful in bringing students and practitioners up to date with the subject.
Release

Data Mining

Practical Machine Learning Tools and Techniques

Author: Ian H. Witten,Eibe Frank,Mark A. Hall,Christopher J. Pal

Publisher: Morgan Kaufmann

ISBN: 0128043571

Category: Computers

Page: 654

View: 3747

Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches. Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research. Please visit the book companion website at http://www.cs.waikato.ac.nz/ml/weka/book.html It contains Powerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the book Online Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the book Table of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc. Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projects Presents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods Includes a downloadable WEKA software toolkit, a comprehensive collection of machine learning algorithms for data mining tasks-in an easy-to-use interactive interface Includes open-access online courses that introduce practical applications of the material in the book
Release

World Regional Geography

Global Patterns, Local Lives

Author: Lydia Mihelic Pulsipher,Alex Pulsipher

Publisher: Macmillan

ISBN: 1429232412

Category: Science

Page: 625

View: 561

Like no other textbook, Pulsipher and Pulsipher’s World Regional Geography puts a human face on the study of regional geography, showing how larger geographical forces affect the lives of individuals and communities around the globe. It’s a refreshing, people-centered approach to the subject focusing on the stories of real people, global trends and interregional linkages, and contemporary topics that transcend regional borders (the war on terrorism, global political order, interregional trade, the global economy, popular culture, the environment, and the Internet).
Release

Machine Learning and Data Mining in Pattern Recognition

4th International Conference, MLDM 2005, Leipzig, Germany, July 9-11, 2005, Proceedings

Author: Petra Perner,Atsushi Imiya

Publisher: Springer Science & Business Media

ISBN: 3540269231

Category: Computers

Page: 698

View: 5302

We met again in front of the statue of Gottfried Wilhelm von Leibniz in the city of Leipzig. Leibniz, a famous son of Leipzig, planned automatic logical inference using symbolic computation, aimed to collate all human knowledge. Today, artificial intelligence deals with large amounts of data and knowledge and finds new information using machine learning and data mining. Machine learning and data mining are irreplaceable subjects and tools for the theory of pattern recognition and in applications of pattern recognition such as bioinformatics and data retrieval. This was the fourth edition of MLDM in Pattern Recognition which is the main event of Technical Committee 17 of the International Association for Pattern Recognition; it started out as a workshop and continued as a conference in 2003. Today, there are many international meetings which are titled “machine learning” and “data mining”, whose topics are text mining, knowledge discovery, and applications. This meeting from the first focused on aspects of machine learning and data mining in pattern recognition problems. We planned to reorganize classical and well-established pattern recognition paradigms from the viewpoints of machine learning and data mining. Though it was a challenging program in the late 1990s, the idea has inspired new starting points in pattern recognition and effects in other areas such as cognitive computer vision.
Release