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Introduction To Pattern Recognition

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Introduction to Pattern Recognition

Introduction to Pattern Recognition Book
Author : Sergios Theodoridis,Aggelos Pikrakis,Konstantinos Koutroumbas,Dionisis Cavouras
Publisher : Academic Press
Release : 2010-03-03
ISBN : 9780080922751
Language : En, Es, Fr & De

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Book Description :

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)

Introduction to Statistical Pattern Recognition

Introduction to Statistical Pattern Recognition Book
Author : Keinosuke Fukunaga
Publisher : Elsevier
Release : 2013-10-22
ISBN : 0080478654
Language : En, Es, Fr & De

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Book Description :

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.

Introduction to Pattern Recognition

Introduction to Pattern Recognition Book
Author : Menahem Friedman,Abraham Kandel
Publisher : World Scientific
Release : 1999
ISBN : 9789810233129
Language : En, Es, Fr & De

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Book Description :

This book is an introduction to pattern recognition, meant for undergraduate and graduate students in computer science and related fields in science and technology. Most of the topics are accompanied by detailed algorithms and real world applications. In addition to statistical and structural approaches, novel topics such as fuzzy pattern recognition and pattern recognition via neural networks are also reviewed. Each topic is followed by several examples solved in detail. The only prerequisites for using this book are a one-semester course in discrete mathematics and a knowledge of the basic preliminaries of calculus, linear algebra and probability theory.

Pattern Recognition and Classification

Pattern Recognition and Classification Book
Author : Geoff Dougherty
Publisher : Springer Science & Business Media
Release : 2012-10-28
ISBN : 1461453232
Language : En, Es, Fr & De

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Book Description :

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

Pattern Recognition Book
Author : Jürgen Beyerer,Matthias Richter,Matthias Nagel
Publisher : Walter de Gruyter GmbH & Co KG
Release : 2017-12-04
ISBN : 3110537966
Language : En, Es, Fr & De

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Book Description :

The book offers a thorough introduction to Pattern Recognition aimed at master and advanced bachelor students of engineering and the natural sciences. Besides classification - the heart of Pattern Recognition - special emphasis is put on features, their typology, their properties and their systematic construction. Additionally, general principles that govern Pattern Recognition are illustrated and explained in a comprehensible way. Rather than presenting a complete overview over the rapidly evolving field, the book is to clarifies the concepts so that the reader can easily understand the underlying ideas and the rationale behind the methods. For this purpose, the mathematical treatment of Pattern Recognition is pushed so far that the mechanisms of action become clear and visible, but not farther. Therefore, not all derivations are driven into the last mathematical detail, as a mathematician would expect it. Ideas of proofs are presented instead of complete proofs. From the authors’ point of view, this concept allows to teach the essential ideas of Pattern Recognition with sufficient depth within a relatively lean book. Mathematical methods explained thoroughly Extremely practical approach with many examples Based on over ten years lecture at Karlsruhe Institute of Technology For students but also for practitioners

Statistical Pattern Recognition

Statistical Pattern Recognition Book
Author : Andrew R. Webb
Publisher : John Wiley & Sons
Release : 2003-07-25
ISBN : 0470854782
Language : En, Es, Fr & De

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Book Description :

Statistical pattern recognition is a very active area of study and research, which has seen many advances in recent years. New and emerging applications - such as data mining, web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition - require robust and efficient pattern recognition techniques. Statistical decision making and estimation are regarded as fundamental to the study of pattern recognition. Statistical Pattern Recognition, Second Edition has been fully updated with new methods, applications and references. It provides a comprehensive introduction to this vibrant area - with material drawn from engineering, statistics, computer science and the social sciences - and covers many application areas, such as database design, artificial neural networks, and decision support systems. * Provides a self-contained introduction to statistical pattern recognition. * Each technique described is illustrated by real examples. * Covers Bayesian methods, neural networks, support vector machines, and unsupervised classification. * Each section concludes with a description of the applications that have been addressed and with further developments of the theory. * Includes background material on dissimilarity, parameter estimation, data, linear algebra and probability. * Features a variety of exercises, from 'open-book' questions to more lengthy projects. The book is aimed primarily at senior undergraduate and graduate students studying statistical pattern recognition, pattern processing, neural networks, and data mining, in both statistics and engineering departments. It is also an excellent source of reference for technical professionals working in advanced information development environments.

Introduction to Recognition and Deciphering of Patterns

Introduction to Recognition and Deciphering of Patterns Book
Author : Michael A. Radin
Publisher : CRC Press
Release : 2020-08-10
ISBN : 1000078558
Language : En, Es, Fr & De

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Book Description :

Introduction to Recognition and Deciphering of Patterns is meant to acquaint STEM and non-STEM students with different patterns, as well as to where and when specific patterns arise. In addition, the book teaches students how to recognize patterns and distinguish the similarities and differences between them. Patterns, such as weather patterns, traffic patterns, behavioral patterns, geometric patterns, linguistic patterns, structural patterns, digital patterns, and the like, emerge on an everyday basis, . Recognizing patterns and studying their unique traits are essential for the development and enhancement of our intuitive skills and for strengthening our analytical skills. Mathematicians often apply patterns to get acquainted with new concepts--a technique that can be applied across many disciplines. Throughout this book we explore assorted patterns that emerge from various geometrical configurations of squares, circles, right triangles, and equilateral triangles that either repeat at the same scale or at different scales. The book also analytically examines linear patterns, geometric patterns, alternating patterns, piecewise patterns, summation-type patterns and factorial-type patterns. Deciphering the details of these distinct patterns leads to the proof by induction method, and the book will also render properties of Pascal’s triangle and provide supplemental practice in deciphering specific patterns and verifying them. This book concludes with first-order recursive relations: describing sequences as recursive relations, obtaining the general solution by solving an initial value problem, and determining the periodic traits. Features • Readily accessible to a broad audience, including those with limited mathematical background • Especially useful for students in non-STEM disciplines, such as psychology, sociology, economics and business, as well as for liberal arts disciplines and art students.

Pattern Recognition

Pattern Recognition Book
Author : Sergios Theodoridis,Konstantinos Koutroumbas
Publisher : Elsevier
Release : 2003-05-15
ISBN : 9780080513621
Language : En, Es, Fr & De

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Book Description :

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 Pattern Recognition and Machine Learning

Introduction to Pattern Recognition and Machine Learning Book
Author : M Narasimha Murty,V Susheela Devi
Publisher : World Scientific
Release : 2015-04-22
ISBN : 9814656275
Language : En, Es, Fr & De

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Book Description :

This book adopts a detailed and methodological algorithmic approach to explain the concepts of pattern recognition. While the text provides a systematic account of its major topics such as pattern representation and nearest neighbour based classifiers, current topics — neural networks, support vector machines and decision trees — attributed to the recent vast progress in this field are also dealt with. Introduction to Pattern Recognition and Machine Learning will equip readers, especially senior computer science undergraduates, with a deeper understanding of the subject matter. Contents:IntroductionTypes of DataFeature Extraction and Feature SelectionBayesian LearningClassificationClassification Using Soft Computing TechniquesData ClusteringSoft ClusteringApplication — Social and Information Networks Readership: Academics and working professionals in computer science. Key Features:The algorithmic approach taken and the practical issues dealt with will aid the reader in writing programs and implementing methodsCovers recent and advanced topics by providing working exercises, examples and illustrations in each chapterProvides the reader with a deeper understanding of the subject matterKeywords:Clustering;Classification;Supervised Learning;Soft Computing

Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning Book
Author : Y. Anzai
Publisher : Elsevier
Release : 2012-12-02
ISBN : 0080513638
Language : En, Es, Fr & De

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Book Description :

This is the first text to provide a unified and self-contained introduction to visual pattern recognition and machine learning. It is useful as a general introduction to artifical intelligence and knowledge engineering, and no previous knowledge of pattern recognition or machine learning is necessary. Basic for various pattern recognition and machine learning methods. Translated from Japanese, the book also features chapter exercises, keywords, and summaries.

Syntactic Pattern Recognition Applications

Syntactic Pattern Recognition  Applications Book
Author : K.S. Fu
Publisher : Springer Science & Business Media
Release : 2012-12-06
ISBN : 3642664385
Language : En, Es, Fr & De

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Book Description :

The many different mathematical techniques used to solve pattem recognition problems may be grouped into two general approaches: the decision-theoretic (or discriminant) approach and the syntactic (or structural) approach. In the decision-theoretic approach, aset of characteristic measurements, called features, are extracted from the pattems. Each pattem is represented by a feature vector, and the recognition of each pattem is usually made by partitioning the feature space. Applications of decision-theoretic approach indude character recognition, medical diagnosis, remote sensing, reliability and socio-economics. A relatively new approach is the syntactic approach. In the syntactic approach, ea ch pattem is expressed in terms of a composition of its components. The recognition of a pattem is usually made by analyzing the pattem structure according to a given set of rules. Earlier applications of the syntactic approach indude chromosome dassification, English character recognition and identification of bubble and spark chamber events. The purpose of this monograph is to provide a summary of the major reeent applications of syntactic pattem recognition. After a brief introduction of syntactic pattem recognition in Chapter 1, the nin e mai n chapters (Chapters 2-10) can be divided into three parts. The first three chapters concem with the analysis of waveforms using syntactic methods. Specific application examples indude peak detection and interpretation of electro cardiograms and the recognition of speech pattems. The next five chapters deal with the syntactic recognition of two-dimensional pictorial pattems.

Pattern Recognition

Pattern Recognition Book
Author : M. Narasimha Murty,V. Susheela Devi
Publisher : Springer Science & Business Media
Release : 2011-05-25
ISBN : 0857294954
Language : En, Es, Fr & De

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Book Description :

Observing the environment and recognising patterns for the purpose of decision making is fundamental to human nature. This book deals with the scientific discipline that enables similar perception in machines through pattern recognition (PR), which has application in diverse technology areas. This book is an exposition of principal topics in PR using an algorithmic approach. It provides a thorough introduction to the concepts of PR and a systematic account of the major topics in PR besides reviewing the vast progress made in the field in recent times. It includes basic techniques of PR, neural networks, support vector machines and decision trees. While theoretical aspects have been given due coverage, the emphasis is more on the practical. The book is replete with examples and illustrations and includes chapter-end exercises. It is designed to meet the needs of senior undergraduate and postgraduate students of computer science and allied disciplines.

Neural Networks for Pattern Recognition

Neural Networks for Pattern Recognition Book
Author : Christopher M. Bishop
Publisher : Oxford University Press
Release : 1995-11-23
ISBN : 0198538642
Language : En, Es, Fr & De

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Book Description :

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

Fundamentals of Pattern Recognition and Machine Learning

Fundamentals of Pattern Recognition and Machine Learning Book
Author : Ulisses Braga-Neto
Publisher : Springer Nature
Release : 2020-09-10
ISBN : 3030276562
Language : En, Es, Fr & De

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Book Description :

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.

Syntactic Pattern Recognition

Syntactic Pattern Recognition Book
Author : Rafael C Gonzalez, PH.D.,Michael G. Thomason
Publisher : Addison-Wesley
Release : 1978
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Syntactic Pattern Recognition book written by Rafael C Gonzalez, PH.D.,Michael G. Thomason, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Decision Estimation and Classification

Decision Estimation and Classification Book
Author : Charles W. Therrien
Publisher : John Wiley & Sons Incorporated
Release : 1989-01-17
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

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

PATTERN RECOGNITION

PATTERN RECOGNITION Book
Author : Syed Thouheed Ahmed,Syed Muzamil Basha,Sajeev Ram Arumugam,Mallikarjun M Kodabagi
Publisher : MileStone Research Publications
Release : 2021-08-01
ISBN : 9354931375
Language : En, Es, Fr & De

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Book Description :

This book covers the primary and supportive topics on pattern recognition with respect to beginners understand-ability. The aspects of pattern recognition is value added with an introductory of machine learning terminologies. This book covers the aspects of pattern validation, recognition, computation and processing. The initial aspects such as data representation and feature extraction is reported with supportive topics such as computational algorithms and decision trees. This text book covers the aspects as reported. Par t - I In this part, the initial foundation aspects of pattern recognition is discussed with reference to probabilities role in influencing a pattern occurrence, pattern extraction and properties. Introduction: Definition of Pattern Recognition, Applications, Datasets for Pattern Recognition, Different paradigms for Pattern Recognition, Introduction to probability, events, random variables, Joint distributions and densities, moments. Estimation minimum risk estimators, problems. Representation: Data structures for Pattern Recognition, Representation of clusters, proximity measures, size of patterns, Abstraction of Data set, Feature extraction, Feature selection, Evaluation. Par t - II In Part - II of the text, the mathematical representation and computation algorithms for extracting and evaluating patterns are discussed. The basic algorithms of machine learning classifiers with Nearest neighbor and Naive Bayes is reported with value added validation process using decision trees. Computational Algorithms: Nearest neighbor algorithm, variants of NN algorithms, use of NN for transaction databases, efficient algorithms, Data reduction, prototype selection, Bayes theorem, minimum error rate classifier, estimation of probabilities, estimation of probabilities, comparison with NNC, Naive Bayesclassifier, Bayesian belief network. Decision Trees: Introduction, Decision Tree for Pattern Recognition, Construction of Decision Tree, Splittingat the nodes, Over-fitting& Pruning, Examples.

Introduction to Pattern Recognition

Introduction to Pattern Recognition Book
Author : Menahem Friedman,Abraham Kandel
Publisher : World Scientific Publishing Company
Release : 1999-03-01
ISBN : 9813105186
Language : En, Es, Fr & De

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Book Description :

This book is an introduction to pattern recognition, meant for undergraduate and graduate students in computer science and related fields in science and technology. Most of the topics are accompanied by detailed algorithms and real world applications. In addition to statistical and structural approaches, novel topics such as fuzzy pattern recognition and pattern recognition via neural networks are also reviewed. Each topic is followed by several examples solved in detail. The only prerequisites for using this book are a one-semester course in discrete mathematics and a knowledge of the basic preliminaries of calculus, linear algebra and probability theory.

Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning Book
Author : Christopher M. Bishop
Publisher : Springer
Release : 2016-08-23
ISBN : 9781493938438
Language : En, Es, Fr & De

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Book Description :

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.

Advanced Lectures on Machine Learning

Advanced Lectures on Machine Learning Book
Author : Olivier Bousquet,Ulrike von Luxburg,Gunnar Rätsch
Publisher : Springer Science & Business Media
Release : 2004-09-02
ISBN : 3540231226
Language : En, Es, Fr & De

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Book Description :

Machine Learning has become a key enabling technology for many engineering applications, investigating scientific questions and theoretical problems alike. To stimulate discussions and to disseminate new results, a summer school series was started in February 2002, the documentation of which is published as LNAI 2600. This book presents revised lectures of two subsequent summer schools held in 2003 in Canberra, Australia, and in Tübingen, Germany. The tutorial lectures included are devoted to statistical learning theory, unsupervised learning, Bayesian inference, and applications in pattern recognition; they provide in-depth overviews of exciting new developments and contain a large number of references. Graduate students, lecturers, researchers and professionals alike will find this book a useful resource in learning and teaching machine learning.