Pattern Recognition And Classification

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Pattern Recognition and Classification

Geoff Dougherty
Pattern Recognition and Classification Book PDF

Author : Geoff Dougherty
Publisher : Springer Science & Business Media
Page : 203 pages
File Size : 40,5 Mb
Release : 2012-10-28
Category : Computers
ISBN : 9781461453239

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Pattern Recognition and Classification by Geoff Dougherty Pdf

The use of pattern recognition and classification is fundamental to many of the automated electronic systems in use today. However, despite the existence of a number of notable books in the field, the subject remains very challenging, especially for the beginner. Pattern Recognition and Classification presents a comprehensive introduction to the core concepts involved in automated pattern recognition. It is designed to be accessible to newcomers from varied backgrounds, but it will also be useful to researchers and professionals in image and signal processing and analysis, and in computer vision. Fundamental concepts of supervised and unsupervised classification are presented in an informal, rather than axiomatic, treatment so that the reader can quickly acquire the necessary background for applying the concepts to real problems. More advanced topics, such as semi-supervised classification, combining clustering algorithms and relevance feedback are addressed in the later chapters. This book is suitable for undergraduates and graduates studying pattern recognition and machine learning.

Pattern Recognition and Classification in Time Series Data

Volna, Eva,Kotyrba, Martin,Janosek, Michal
Pattern Recognition and Classification in Time Series Data Book PDF

Author : Volna, Eva,Kotyrba, Martin,Janosek, Michal
Publisher : IGI Global
Page : 282 pages
File Size : 46,8 Mb
Release : 2016-07-22
Category : Computers
ISBN : 9781522505662

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Pattern Recognition and Classification in Time Series Data by Volna, Eva,Kotyrba, Martin,Janosek, Michal Pdf

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.

Pattern Classification

Richard O. Duda,Peter E. Hart,David G. Stork
Pattern Classification Book PDF

Author : Richard O. Duda,Peter E. Hart,David G. Stork
Publisher : John Wiley & Sons
Page : 680 pages
File Size : 53,6 Mb
Release : 2012-11-09
Category : Technology & Engineering
ISBN : 9781118586006

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Pattern Classification by Richard O. Duda,Peter E. Hart,David G. Stork Pdf

The first edition, published in 1973, has become a classicreference in the field. Now with the second edition, readers willfind information on key new topics such as neural networks andstatistical pattern recognition, the theory of machine learning,and the theory of invariances. Also included are worked examples,comparisons between different methods, extensive graphics, expandedexercises and computer project topics. An Instructor's Manual presenting detailed solutions to all theproblems in the book is available from the Wiley editorialdepartment.

Data Complexity in Pattern Recognition

Mitra Basu,Tin Kam Ho
Data Complexity in Pattern Recognition Book PDF

Author : Mitra Basu,Tin Kam Ho
Publisher : Springer Science & Business Media
Page : 309 pages
File Size : 51,7 Mb
Release : 2006-12-22
Category : Computers
ISBN : 9781846281723

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Data Complexity in Pattern Recognition by Mitra Basu,Tin Kam Ho Pdf

Automatic pattern recognition has uses in science and engineering, social sciences and finance. This book examines data complexity and its role in shaping theory and techniques across many disciplines, probing strengths and deficiencies of current classification techniques, and the algorithms that drive them. The book offers guidance on choosing pattern recognition classification techniques, and helps the reader set expectations for classification performance.

Principles of Nonparametric Learning

Laszlo Györfi
Principles of Nonparametric Learning Book PDF

Author : Laszlo Györfi
Publisher : Springer
Page : 344 pages
File Size : 42,6 Mb
Release : 2014-05-04
Category : Technology & Engineering
ISBN : 9783709125687

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Principles of Nonparametric Learning by Laszlo Györfi Pdf

This volume provides a systematic in-depth analysis of nonparametric learning. It covers the theoretical limits and the asymptotical optimal algorithms and estimates, such as pattern recognition, nonparametric regression estimation, universal prediction, vector quantization, distribution and density estimation, and genetic programming.

Pattern Classification

Jgen Schmann
Pattern Classification Book PDF

Author : Jgen Schmann
Publisher : Wiley-Interscience
Page : 424 pages
File Size : 48,9 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.

Invariants for Pattern Recognition and Classification

Marcos A. Rodrigues
Invariants for Pattern Recognition and Classification Book PDF

Author : Marcos A. Rodrigues
Publisher : World Scientific
Page : 249 pages
File Size : 48,7 Mb
Release : 2000
Category : Science
ISBN : 9789810242787

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Invariants for Pattern Recognition and Classification by Marcos A. Rodrigues Pdf

This book was conceived from the realization that there was a need to update recent work on invariants in a single volume providing a useful set of references and pointers to related work. Since the publication in 1992 of J L Mundy and A Zisserman's Geometric Invariance in Computer Vision, the subject has been evolving rapidly. New approaches to invariants have been proposed and novel ways of defining and applying invariants to practical problem solving are testimony to the fundamental importance of the study of invariants to machine vision. This book represents a snapshot of current research around the world. A version of this collection of papers has appeared in the International Journal of Pattern Recognition and Artificial Intelligence (December 1999). The papers in this book are extended versions of the original material published in the journal. They are organized into two categories: foundations and applications. Foundation papers present new ways of defining or analyzing invariants, andapplication papers present novel ways in which known invariant theory is extended and effectively applied to real-world problems in interesting and difficult contexts. Each category contains roughly half of the papers, but there is considerable overlap. All papers carry an element of novelty and generalization that will be useful to theoreticians and practitioners alike. It is hoped that this volume will be not only useful but also inspirational to researchers in image processing, pattern recognition and computer vision at large.

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 : 44,6 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.

Fundamentals of Pattern Recognition and Machine Learning

Ulisses Braga-Neto
Fundamentals of Pattern Recognition and Machine Learning Book PDF

Author : Ulisses Braga-Neto
Publisher : Springer Nature
Page : 357 pages
File Size : 51,5 Mb
Release : 2020-09-10
Category : Computers
ISBN : 9783030276560

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Fundamentals of Pattern Recognition and Machine Learning by Ulisses Braga-Neto Pdf

Fundamentals of Pattern Recognition and Machine Learning is designed for a one or two-semester introductory course in Pattern Recognition or Machine Learning at the graduate or advanced undergraduate level. The book combines theory and practice and is suitable to the classroom and self-study. It has grown out of lecture notes and assignments that the author has developed while teaching classes on this topic for the past 13 years at Texas A&M University. The book is intended to be concise but thorough. It does not attempt an encyclopedic approach, but covers in significant detail the tools commonly used in pattern recognition and machine learning, including classification, dimensionality reduction, regression, and clustering, as well as recent popular topics such as Gaussian process regression and convolutional neural networks. In addition, the selection of topics has a few features that are unique among comparable texts: it contains an extensive chapter on classifier error estimation, as well as sections on Bayesian classification, Bayesian error estimation, separate sampling, and rank-based classification. The book is mathematically rigorous and covers the classical theorems in the area. Nevertheless, an effort is made in the book to strike a balance between theory and practice. In particular, examples with datasets from applications in bioinformatics and materials informatics are used throughout to illustrate the theory. These datasets are available from the book website to be used in end-of-chapter coding assignments based on python and scikit-learn. All plots in the text were generated using python scripts, which are also available on the book website.

Pattern Classification

Shigeo Abe
Pattern Classification Book PDF

Author : Shigeo Abe
Publisher : Springer Science & Business Media
Page : 332 pages
File Size : 53,6 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9781447102854

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Pattern Classification by Shigeo Abe Pdf

This book provides a unified approach for developing a fuzzy classifier and explains the advantages and disadvantages of different classifiers through extensive performance evaluation of real data sets. It thus offers new learning paradigms for analyzing neural networks and fuzzy systems, while training fuzzy classifiers. Function approximation is also treated and function approximators are compared.

Decision Estimation and Classification

Charles W. Therrien
Decision Estimation and Classification Book PDF

Author : Charles W. Therrien
Publisher : Unknown
Page : 280 pages
File Size : 46,9 Mb
Release : 1989-01-17
Category : Computers
ISBN : UOM:39076001111413

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Decision Estimation and Classification by Charles W. Therrien Pdf

Very Good,No Highlights or Markup,all pages are intact.

Pattern Recognition

Sergios Theodoridis,Konstantinos Koutroumbas
Pattern Recognition Book PDF

Author : Sergios Theodoridis,Konstantinos Koutroumbas
Publisher : Elsevier
Page : 689 pages
File Size : 47,9 Mb
Release : 2003-05-15
Category : Technology & Engineering
ISBN : 008051362X

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Pattern Recognition by Sergios Theodoridis,Konstantinos Koutroumbas Pdf

Pattern recognition is a scientific discipline that is becoming increasingly important in the age of automation and information handling and retrieval. Patter Recognition, 2e covers the entire spectrum of pattern recognition applications, from image analysis to speech recognition and communications. This book presents cutting-edge material on neural networks, - a set of linked microprocessors that can form associations and uses pattern recognition to "learn" -and enhances student motivation by approaching pattern recognition from the designer's point of view. A direct result of more than 10 years of teaching experience, the text was developed by the authors through use in their own classrooms. *Approaches pattern recognition from the designer's point of view *New edition highlights latest developments in this growing field, including independent components and support vector machines, not available elsewhere *Supplemented by computer examples selected from applications of interest

Introduction to Statistical Pattern Recognition

Keinosuke Fukunaga
Introduction to Statistical Pattern Recognition Book PDF

Author : Keinosuke Fukunaga
Publisher : Elsevier
Page : 592 pages
File Size : 51,6 Mb
Release : 2013-10-22
Category : Computers
ISBN : 9780080478654

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Introduction to Statistical Pattern Recognition by Keinosuke Fukunaga Pdf

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.

A Probabilistic Theory of Pattern Recognition

Luc Devroye,Laszlo Györfi,Gabor Lugosi
A Probabilistic Theory of Pattern Recognition Book PDF

Author : Luc Devroye,Laszlo Györfi,Gabor Lugosi
Publisher : Springer Science & Business Media
Page : 631 pages
File Size : 46,9 Mb
Release : 2013-11-27
Category : Mathematics
ISBN : 9781461207115

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A Probabilistic Theory of Pattern Recognition by Luc Devroye,Laszlo Györfi,Gabor Lugosi Pdf

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.

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 : 40,8 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.