Pattern Classification

Author: Richard O. Duda
Publisher: John Wiley & Sons
ISBN: 9781118586006
Release Date: 2012-11-09
Genre: Technology & Engineering

The first edition, published in 1973, has become a classic reference in the field. Now with the second edition, readers will find information on key new topics such as neural networks and statistical pattern recognition, the theory of machine learning, and the theory of invariances. Also included are worked examples, comparisons between different methods, extensive graphics, expanded exercises and computer project topics. An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.

Pattern classification

Author: Jürgen Schürmann
Publisher: Wiley-Interscience
ISBN: 0471135348
Release Date: 1996-03-15
Genre: Computers

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.

A Probabilistic Theory of Pattern Recognition

Author: Luc Devroye
Publisher: Springer Science & Business Media
ISBN: 9781461207115
Release Date: 2013-11-27
Genre: Mathematics

A self-contained and coherent account of probabilistic techniques, covering: distance measures, kernel rules, nearest neighbour rules, Vapnik-Chervonenkis theory, parametric classification, and feature extraction. Each chapter concludes with problems and exercises to further the readers understanding. Both research workers and graduate students will benefit from this wide-ranging and up-to-date account of a fast- moving field.

Pattern Recognition

Author: William Gibson
Publisher: Penguin UK
ISBN: 9780141904467
Release Date: 2004-06-24
Genre: Fiction

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.

Multivariate Pattern Recognition in Chemometrics

Author: R.G. Brereton
Publisher: Elsevier
ISBN: 0080868363
Release Date: 1992-09-04
Genre: Technology & Engineering

Chemometrics originated from multivariate statistics in chemistry, and this field is still the core of the subject. The increasing availability of user-friendly software in the laboratory has prompted the need to optimize it safely. This work comprises material presented in courses organized from 1987-1992, aimed mainly at professionals in industry. The book covers approaches for pattern recognition as applied, primarily, to multivariate chemical data. These include data reduction and display techniques, principal components analysis and methods for classification and clustering. Comprehensive case studies illustrate the book, including numerical examples, and extensive problems are interspersed throughout the text. The book contains extensive cross-referencing between various chapters, comparing different notations and approaches, enabling readers from different backgrounds to benefit from it and to move around chapters at will. Worked examples and exercises are given, making the volume valuable for courses. Tutorial versions of SPECTRAMAP and SIRIUS are optionally available as a Software Supplement, at a low price, to accompany the text.

Handbook of Pattern Recognition and Computer Vision

Author: Chi-hau Chen
Publisher: World Scientific
ISBN: 9789814273398
Release Date: 2010
Genre: Computers

Both pattern recognition and computer vision have experienced rapid progress in the last twenty-five years. This book provides the latest advances on pattern recognition and computer vision along with their many applications. It features articles written by renowned leaders in the field while topics are presented in readable form to a wide range of readers. The book is divided into five parts: basic methods in pattern recognition, basic methods in computer vision and image processing, recognition applications, life science and human identification, and systems and technology.

Pattern Recognition and Machine Learning

Author: Christopher M. Bishop
Publisher: Springer
ISBN: 1493938436
Release Date: 2016-08-23
Genre: Computers

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.

Pattern Classification

Author: Richard O. Duda
Publisher: John Wiley & Sons
ISBN: 9781118586006
Release Date: 2012-11-09
Genre: Technology & Engineering

The first edition, published in 1973, has become a classic reference in the field. Now with the second edition, readers will find information on key new topics such as neural networks and statistical pattern recognition, the theory of machine learning, and the theory of invariances. Also included are worked examples, comparisons between different methods, extensive graphics, expanded exercises and computer project topics. An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.

Pattern Recognition and Image Analysis

Author: Jorge S. Marques
Publisher: Springer
ISBN: 9783540322375
Release Date: 2005-05-13
Genre: Computers

IbPRIA 2005 (Iberian Conference on Pattern Recognition and Image Analysis) was the second of a series of conferences jointly organized every two years by the Portuguese and Spanish Associations for Pattern Recognition (APRP, AERFAI), with the support of the International Association for Pattern Recognition (IAPR). This year, IbPRIA was hosted by the Institute for Systems and Robotics and the Geo-systems Center of the Instituto Superior Tecn ́ ico and it was held in Estoril, Por- gal. It provided the opportunity to bring together researchers from all over the world to discuss some of the most recent advances in pattern recognition and all areas of video, image and signal processing. There was a very positive response to the Call for Papers for IbPRIA 2005. We - ceived 292 full papers from 38 countries and 170 were accepted for presentation at the conference. The high quality of the scienti?c program of IbPRIA 2005 was due ?rst to the authors who submitted excellent contributions and second to the dedicated colla- ration of the international Program Committee and the other researchers who reviewed the papers. Each paper was reviewed by two reviewers, in a blind process. We would like to thank all the authors for submitting their contributions and for sharing their - search activities. We are particularly indebted to the Program Committee members and to all the reviewers for their precious evaluations, which permitted us to set up this publication.

Pattern Recognition

Author: Wladyslaw Homenda
Publisher: John Wiley & Sons
ISBN: 9781119302827
Release Date: 2018-03-07
Genre: Computers

A new approach to the issue of data quality in pattern recognition Detailing foundational concepts before introducing more complex methodologies and algorithms, this book is a self-contained manual for advanced data analysis and data mining. Top-down organization presents detailed applications only after methodological issues have been mastered, and step-by-step instructions help ensure successful implementation of new processes. By positioning data quality as a factor to be dealt with rather than overcome, the framework provided serves as a valuable, versatile tool in the analysis arsenal. For decades, practical need has inspired intense theoretical and applied research into pattern recognition for numerous and diverse applications. Throughout, the limiting factor and perpetual problem has been data—its sheer diversity, abundance, and variable quality presents the central challenge to pattern recognition innovation. Pattern Recognition: A Quality of Data Perspective repositions that challenge from a hurdle to a given, and presents a new framework for comprehensive data analysis that is designed specifically to accommodate problem data. Designed as both a practical manual and a discussion about the most useful elements of pattern recognition innovation, this book: Details fundamental pattern recognition concepts, including feature space construction, classifiers, rejection, and evaluation Provides a systematic examination of the concepts, design methodology, and algorithms involved in pattern recognition Includes numerous experiments, detailed schemes, and more advanced problems that reinforce complex concepts Acts as a self-contained primer toward advanced solutions, with detailed background and step-by-step processes Introduces the concept of granules and provides a framework for granular computing Pattern recognition plays a pivotal role in data analysis and data mining, fields which are themselves being applied in an expanding sphere of utility. By facing the data quality issue head-on, this book provides students, practitioners, and researchers with a clear way forward amidst the ever-expanding data supply.

Olives and Olive Oil in Health and Disease Prevention

Author: Victor R. Preedy
Publisher: Academic Press
ISBN: 0080922201
Release Date: 2010-03-23
Genre: Technology & Engineering

Long used in sacred ceremonies and associated with good health, the nutritional and health promoting benefits of olives and olive oils have been proven by an ever-increasing body of science. From cardiovascular benefits to anti-microbial, anti-cancer, antioxidant activity and effects on macrophages and aptoptosis to cellular and pathophysiollogical process, olives and olive oils are proving important in many healthful ways. For example, reactive components in olive oils or olive oil by-products have now been isolated and identified. These include tyrosol, hydroxytyrosol, 3,4-dihydroxyphenyl acetic acid elenolic acid and oleuropein. Oleic acid is the main monosaturated fatty acid of olive oil. These have putative protective effects and modulate the biochemistry of a variety of cell types including those of the vascular system. Some but not all components have been characterised by their putative pharmacological properties. It is possible that usage of these aforementioned products may have beneficial application in other disease. However, in order for this cross-fertilization to take place, a comprehensive understanding of olives and olive oils is required. Finding this knowledge in a single volume provides a key resource for scientists in a variety of food an nutritional roles. Key Features: * Explores olives and olive oil from their general aspects to the detailed level of important micro-and micronutrients * Includes coverage of various methodologies for analysis to help scientists and chemists determine the most appropriate option for their own studies, including those of olive-related compounds in other foods * Relates, in a single volume resource, information for food and nutritional chemists, pharmaceutical scientists, nutritionists and dieticians * Presents information in three key categories: General aspects of olives an olive oils; Nutritional, pharmacological and metabolic properties of olives and olive oil; Specific components of olive oil and their effects on tissue and body systems

Introduction to Statistical Machine Learning

Author: Masashi Sugiyama
Publisher: Morgan Kaufmann
ISBN: 9780128023501
Release Date: 2015-10-31
Genre: Computers

Machine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and machine learning are combined together they are a powerful tool for analysing various kinds of data in many computer science/engineering areas including, image processing, speech processing, natural language processing, robot control, as well as in fundamental sciences such as biology, medicine, astronomy, physics, and materials. Introduction to Statistical Machine Learning provides a general introduction to machine learning that covers a wide range of topics concisely and will help you bridge the gap between theory and practice. Part I discusses the fundamental concepts of statistics and probability that are used in describing machine learning algorithms. Part II and Part III explain the two major approaches of machine learning techniques; generative methods and discriminative methods. While Part III provides an in-depth look at advanced topics that play essential roles in making machine learning algorithms more useful in practice. The accompanying MATLAB/Octave programs provide you with the necessary practical skills needed to accomplish a wide range of data analysis tasks. Provides the necessary background material to understand machine learning such as statistics, probability, linear algebra, and calculus. Complete coverage of the generative approach to statistical pattern recognition and the discriminative approach to statistical machine learning. Includes MATLAB/Octave programs so that readers can test the algorithms numerically and acquire both mathematical and practical skills in a wide range of data analysis tasks Discusses a wide range of applications in machine learning and statistics and provides examples drawn from image processing, speech processing, natural language processing, robot control, as well as biology, medicine, astronomy, physics, and materials.

Conformal Prediction for Reliable Machine Learning

Author: Vineeth Balasubramanian
Publisher: Newnes
ISBN: 9780124017153
Release Date: 2014-04-23
Genre: Computers

The conformal predictions framework is a recent development in machine learning that can associate a reliable measure of confidence with a prediction in any real-world pattern recognition application, including risk-sensitive applications such as medical diagnosis, face recognition, and financial risk prediction. Conformal Predictions for Reliable Machine Learning: Theory, Adaptations and Applications captures the basic theory of the framework, demonstrates how to apply it to real-world problems, and presents several adaptations, including active learning, change detection, and anomaly detection. As practitioners and researchers around the world apply and adapt the framework, this edited volume brings together these bodies of work, providing a springboard for further research as well as a handbook for application in real-world problems. Understand the theoretical foundations of this important framework that can provide a reliable measure of confidence with predictions in machine learning Be able to apply this framework to real-world problems in different machine learning settings, including classification, regression, and clustering Learn effective ways of adapting the framework to newer problem settings, such as active learning, model selection, or change detection