Pattern Recognition and Classification Book

Pattern Recognition and Classification


  • Author : Geoff Dougherty
  • Publisher : Springer Science & Business Media
  • Release Date : 2012-10-28
  • Genre: Computers
  • Pages : 196
  • ISBN 10 : 9781461453239

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Pattern Recognition and Classification Excerpt :

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.

Introduction to Pattern Recognition Book

Introduction to Pattern Recognition


  • Author : Sergios Theodoridis
  • Publisher : Academic Press
  • Release Date : 2010-03-03
  • Genre: Computers
  • Pages : 231
  • ISBN 10 : 0080922759

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Introduction to Pattern Recognition Excerpt :

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 Book
Score: 4.5
From 2 Ratings

Introduction to Statistical Pattern Recognition


  • Author : Keinosuke Fukunaga
  • Publisher : Elsevier
  • Release Date : 2013-10-22
  • Genre: Computers
  • Pages : 592
  • ISBN 10 : 9780080478654

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Introduction to Statistical Pattern Recognition Excerpt :

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.

Fundamentals of Pattern Recognition and Machine Learning Book

Fundamentals of Pattern Recognition and Machine Learning


  • Author : Ulisses Braga-Neto
  • Publisher : Springer
  • Release Date : 2021-09-25
  • Genre: Computers
  • Pages : 357
  • ISBN 10 : 3030276589

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Fundamentals of Pattern Recognition and Machine Learning Excerpt :

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.

Introduction to Pattern Recognition Book
Score: 1
From 1 Ratings

Introduction to Pattern Recognition


  • Author : Menahem Friedman
  • Publisher : World Scientific
  • Release Date : 1999
  • Genre: Computers
  • Pages : 350
  • ISBN 10 : 9810233124

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Introduction to Pattern Recognition Excerpt :

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 Book
Score: 5
From 1 Ratings

Pattern Recognition and Machine Learning


  • Author : Y. Anzai
  • Publisher : Elsevier
  • Release Date : 2012-12-02
  • Genre: Computers
  • Pages : 407
  • ISBN 10 : 9780080513638

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Pattern Recognition and Machine Learning Excerpt :

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.

Introduction to Recognition and Deciphering of Patterns Book

Introduction to Recognition and Deciphering of Patterns


  • Author : Michael A. Radin
  • Publisher : CRC Press
  • Release Date : 2020-08-09
  • Genre: Mathematics
  • Pages : 182
  • ISBN 10 : 9781000078534

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Introduction to Recognition and Deciphering of Patterns Excerpt :

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 Book

Pattern Recognition


  • Author : Jürgen Beyerer
  • Publisher : Walter de Gruyter GmbH & Co KG
  • Release Date : 2017-12-04
  • Genre: Computers
  • Pages : 311
  • ISBN 10 : 9783110537963

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Pattern Recognition Excerpt :

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

Introduction to Pattern Recognition and Machine Learning Book

Introduction to Pattern Recognition and Machine Learning


  • Author : M Narasimha Murty
  • Publisher : World Scientific
  • Release Date : 2015-04-22
  • Genre: Computers
  • Pages : 404
  • ISBN 10 : 9789814656276

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Introduction to Pattern Recognition and Machine Learning Excerpt :

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 Book

Pattern Recognition


  • Author : Sergios Theodoridis
  • Publisher : Elsevier
  • Release Date : 2003-05-15
  • Genre: Technology & Engineering
  • Pages : 689
  • ISBN 10 : 008051362X

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Pattern Recognition Excerpt :

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

Statistical Pattern Recognition Book

Statistical Pattern Recognition


  • Author : Andrew R. Webb
  • Publisher : John Wiley & Sons
  • Release Date : 2003-07-25
  • Genre: Mathematics
  • Pages : 517
  • ISBN 10 : 9780470854785

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Statistical Pattern Recognition Excerpt :

Statistical pattern recognition is a very active area of study andresearch, which has seen many advances in recent years. New andemerging applications - such as data mining, web searching,multimedia data retrieval, face recognition, and cursivehandwriting recognition - require robust and efficient patternrecognition techniques. Statistical decision making and estimationare regarded as fundamental to the study of pattern recognition. Statistical Pattern Recognition, Second Edition has been fullyupdated with new methods, applications and references. It providesa comprehensive introduction to this vibrant area - with materialdrawn from engineering, statistics, computer science and the socialsciences - and covers many application areas, such as databasedesign, artificial neural networks, and decision supportsystems. * Provides a self-contained introduction to statistical patternrecognition. * Each technique described is illustrated by real examples. * Covers Bayesian methods, neural networks, support vectormachines, and unsupervised classification. * Each section concludes with a description of the applicationsthat have been addressed and with further developments of thetheory. * Includes background material on dissimilarity, parameterestimation, data, linear algebra and probability. * Features a variety of exercises, from 'open-book' questions tomore lengthy projects. The book is aimed primarily at senior undergraduate and graduatestudents studying statistical pattern recognition, patternprocessing, neural networks, and data mining, in both statisticsand engineering departments. It is also an excellent source ofreference for technical professionals working in advancedinformation development environments. For further information on the techniques and applicationsdiscussed in this book please visit ahref="http://www.statistical-pattern-recognition.net/"www.statistical-pattern-recognition.net/a

Pattern Recognition Book

Pattern Recognition


  • Author : Brett Anderson
  • Publisher : Scientific e-Resources
  • Release Date : 2019-09-14
  • Genre: Uncategoriezed
  • Pages : null
  • ISBN 10 : 9781839472398

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Pattern Recognition Excerpt :

Watching the environment and recognising patterns with the end goal of basic leadership is central to human instinct. This book manages the logical train that empowers comparable observation in machines through pattern recognition, which has application in differing innovation regions-character recognition, picture handling, modern computerization, web looks, discourse recognition, therapeutic diagnostics, target recognition, space science, remote detecting, information mining, biometric recognizable proof-to give some examples. This book is a composition of central subjects in pattern recognition utilizing an algorithmic approach. It gives a careful prologue to the ideas of pattern recognition and an efficient record of the real points in pattern recognition other than assessing the huge advance made in the field as of late. It incorporates fundamental strategies of pattern recognition, neural systems, bolster vector machines and choice trees. While hypothetical angles have been given due scope, the accentuation is more on the pragmatic. Pattern recognition has application in practically every field of human undertaking including topography, geology, space science and brain research. All the more particularly, it is helpful in bioinformatics, mental investigation, biometrics and a large group of different applications.

Advanced Lectures on Machine Learning Book

Advanced Lectures on Machine Learning


  • Author : Olivier Bousquet
  • Publisher : Springer
  • Release Date : 2011-03-22
  • Genre: Computers
  • Pages : 246
  • ISBN 10 : 9783540286509

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Advanced Lectures on Machine Learning Excerpt :

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.

Decision Estimation and Classification Book

Decision Estimation and Classification


  • Author : Charles W. Therrien
  • Publisher : John Wiley & Sons Incorporated
  • Release Date : 1989-01-17
  • Genre: Computers
  • Pages : 280
  • ISBN 10 : UOM:39076001111413

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Decision Estimation and Classification Excerpt :

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

Pattern Recognition and Machine Learning Book
Score: 3
From 1 Ratings

Pattern Recognition and Machine Learning


  • Author : Christopher M. Bishop
  • Publisher : Springer
  • Release Date : 2016-08-23
  • Genre: Computers
  • Pages : 738
  • ISBN 10 : 1493938436

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Pattern Recognition and Machine Learning Excerpt :

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.