Error Estimation For Pattern Recognition

Author : Ulisses M. Braga Neto,Edward R. Dougherty
Publisher : John Wiley & Sons
Page : 336 pages
File Size : 51,7 Mb
Release : 2015-06-22
Category : Technology & Engineering
ISBN : 9781119079378

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Error Estimation for Pattern Recognition by Ulisses M. Braga Neto,Edward R. Dougherty Pdf

This book is the first of its kind to discuss error estimation with a model-based approach. From the basics of classifiers and error estimators to distributional and Bayesian theory, it covers important topics and essential issues pertaining to the scientific validity of pattern classification. Error Estimation for Pattern Recognition focuses on error estimation, which is a broad and poorly understood topic that reaches all research areas using pattern classification. It includes model-based approaches and discussions of newer error estimators such as bolstered and Bayesian estimators. This book was motivated by the application of pattern recognition to high-throughput data with limited replicates, which is a basic problem now appearing in many areas. The first two chapters cover basic issues in classification error estimation, such as definitions, test-set error estimation, and training-set error estimation. The remaining chapters in this book cover results on the performance and representation of training-set error estimators for various pattern classifiers. Additional features of the book include: • The latest results on the accuracy of error estimation • Performance analysis of re-substitution, cross-validation, and bootstrap error estimators using analytical and simulation approaches • Highly interactive computer-based exercises and end-of-chapter problems This is the first book exclusively about error estimation for pattern recognition. Ulisses M. Braga Neto is an Associate Professor in the Department of Electrical and Computer Engineering at Texas A&M University, USA. He received his PhD in Electrical and Computer Engineering from The Johns Hopkins University. Dr. Braga Neto received an NSF CAREER Award for his work on error estimation for pattern recognition with applications in genomic signal processing. He is an IEEE Senior Member. Edward R. Dougherty is a Distinguished Professor, Robert F. Kennedy ’26 Chair, and Scientific Director at the Center for Bioinformatics and Genomic Systems Engineering at Texas A&M University, USA. He is a fellow of both the IEEE and SPIE, and he has received the SPIE Presidents Award. Dr. Dougherty has authored several books including Epistemology of the Cell: A Systems Perspective on Biological Knowledge and Random Processes for Image and Signal Processing (Wiley-IEEE Press).

Author : Ulisses M. Braga Neto,Edward R. Dougherty
Publisher : John Wiley & Sons
Page : 336 pages
File Size : 41,6 Mb
Release : 2015-07-07
Category : Technology & Engineering
ISBN : 9781118999738

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Error Estimation for Pattern Recognition by Ulisses M. Braga Neto,Edward R. Dougherty Pdf

This book is the first of its kind to discuss error estimation with a model-based approach. From the basics of classifiers and error estimators to distributional and Bayesian theory, it covers important topics and essential issues pertaining to the scientific validity of pattern classification. Error Estimation for Pattern Recognition focuses on error estimation, which is a broad and poorly understood topic that reaches all research areas using pattern classification. It includes model-based approaches and discussions of newer error estimators such as bolstered and Bayesian estimators. This book was motivated by the application of pattern recognition to high-throughput data with limited replicates, which is a basic problem now appearing in many areas. The first two chapters cover basic issues in classification error estimation, such as definitions, test-set error estimation, and training-set error estimation. The remaining chapters in this book cover results on the performance and representation of training-set error estimators for various pattern classifiers. Additional features of the book include: • The latest results on the accuracy of error estimation • Performance analysis of re-substitution, cross-validation, and bootstrap error estimators using analytical and simulation approaches • Highly interactive computer-based exercises and end-of-chapter problems This is the first book exclusively about error estimation for pattern recognition. Ulisses M. Braga Neto is an Associate Professor in the Department of Electrical and Computer Engineering at Texas A&M University, USA. He received his PhD in Electrical and Computer Engineering from The Johns Hopkins University. Dr. Braga Neto received an NSF CAREER Award for his work on error estimation for pattern recognition with applications in genomic signal processing. He is an IEEE Senior Member. Edward R. Dougherty is a Distinguished Professor, Robert F. Kennedy ’26 Chair, and Scientific Director at the Center for Bioinformatics and Genomic Systems Engineering at Texas A&M University, USA. He is a fellow of both the IEEE and SPIE, and he has received the SPIE Presidents Award. Dr. Dougherty has authored several books including Epistemology of the Cell: A Systems Perspective on Biological Knowledge and Random Processes for Image and Signal Processing (Wiley-IEEE Press).

Author : Ulisses Braga-Neto
Publisher : Springer Nature
Page : 357 pages
File Size : 49,8 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.

Author : Charles W. Therrien
Publisher : Unknown
Page : 280 pages
File Size : 52,6 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.

Author : Syed Thouheed Ahmed,Syed Muzamil Basha,Sajeev Ram Arumugam,Mallikarjun M Kodabagi
Publisher : MileStone Research Publications
Page : 156 pages
File Size : 44,9 Mb
Release : 2021-08-01
Category : Technology & Engineering
ISBN : 9789354931376

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PATTERN RECOGNITION by Syed Thouheed Ahmed,Syed Muzamil Basha,Sajeev Ram Arumugam,Mallikarjun M Kodabagi Pdf

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.

Author : Pierre A. Devijver,Josef Kittler
Publisher : Springer Science & Business Media
Page : 531 pages
File Size : 54,7 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9783642830693

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Pattern Recognition Theory and Applications by Pierre A. Devijver,Josef Kittler Pdf

This book is the outcome of a NATO Advanced Study Institute on Pattern Recog nition Theory and Applications held in Spa-Balmoral, Belgium, in June 1986. This Institute was the third of a series which started in 1975 in Bandol, France, at the initia tive of Professors K. S. Fu and A. Whinston, and continued in 1981 in Oxford, UK, with Professors K. S. Fu, J. Kittler and L. -F. Pau as directors. As early as in 1981, plans were made to pursue the series in about 1986 and possibly in Belgium, with Professor K. S. Fu and the present editors as directors. Unfortunately, Ie sort en decida autrement: Professor Fu passed away in the spring of 1985. His sudden death was an irreparable loss to the scientific community and to all those who knew him as an inspiring colleague, a teacher or a dear friend. Soon after, Josef Kittler and I decided to pay a small tribute to his memory by helping some of his plans to materialize. With the support of the NATO Scientific Affairs Division, the Institute became a reality. It was therefore but natural that the proceedings of the Institute be dedicated to him. The book contains most of the papers that were presented at the Institute. Papers are grouped along major themes which hopefully represent the major areas of contem porary research. These are: 1. Statistical methods and clustering techniques 2. Probabilistic relaxation techniques 3. From Markovian to connectionist models 4.

Author : Luc Devroye,Laszlo Györfi,Gabor Lugosi
Publisher : Springer Science & Business Media
Page : 631 pages
File Size : 47,5 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.

Author : Keinosuke Fukunaga
Publisher : Elsevier
Page : 592 pages
File Size : 40,5 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.

Author : Erwin A. Zoeller
Publisher : Unknown
Page : 0 pages
File Size : 50,9 Mb
Release : 2008
Category : Pattern recognition systems
ISBN : 1600217176

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Pattern Recognition by Erwin A. Zoeller Pdf

Pattern Recognition studies the operation and design of systems that recognise patterns in data. It encloses subdisciplines like discriminant analysis, feature extraction, error estimation, cluster analysis (together sometimes called statistical pattern recognition), grammatical inference and parsing (sometimes called syntactical pattern recognition). Important application areas are image analysis, character recognition, speech analysis, man and machine diagnostics, person identification and industrial inspection. This book presents new international research in the field.

Author : Andrew R. Webb
Publisher : John Wiley & Sons
Page : 516 pages
File Size : 48,8 Mb
Release : 2003-07-25
Category : Mathematics
ISBN : 9780470854785

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Statistical Pattern Recognition by Andrew R. Webb Pdf

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

Author : Sergios Theodoridis,Konstantinos Koutroumbas
Publisher : Elsevier
Page : 856 pages
File Size : 40,6 Mb
Release : 2006-04-07
Category : Computers
ISBN : 0080513611

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Pattern Recognition by Sergios Theodoridis,Konstantinos Koutroumbas Pdf

Pattern recognition is a fast growing area with applications in a widely diverse number of fields such as communications engineering, bioinformatics, data mining, content-based database retrieval, to name but a few. This new edition addresses and keeps pace with the most recent advancements in these and related areas. This new edition: a) covers Data Mining, which was not treated in the previous edition, and is integrated with existing material in the book, b) includes new results on Learning Theory and Support Vector Machines, that are at the forefront of today's research, with a lot of interest both in academia and in applications-oriented communities, c) for the first time treats audio along with image applications since in today's world the most advanced applications are treated in a unified way and d) the subject of classifier combinations is treated, since this is a hot topic currently of interest in the pattern recognition community. * The latest results on support vector machines including v-SVM's and their geometric interpretation * Classifier combinations including the Boosting approach * State-of-the-art material for clustering algorithms tailored for large data sets and/or high dimensional data, as required by applications such as web-mining and bioinformatics * Coverage of diverse applications such as image analysis, optical character recognition, channel equalization, speech recognition and audio classification

Author : King-Sun Fu
Publisher : CRC Press
Page : 284 pages
File Size : 51,5 Mb
Release : 2019-07-22
Category : Technology & Engineering
ISBN : 9781351078252

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Applications of Pattern Recognition by King-Sun Fu Pdf

This monograph is intended to cover several major applications of pattern recognition. After a brief introduction to pattern recognition in Chapter 1, the two major approaches, statistical approach and syntactic approach, are reviewed in Chapter 2, and 3, respectively. Other topics include the application of pattern recognition to seismic wave interpretation, to system reliability problems, to medical data analysis, as well as character and speech recognition.

Author : Stephen Jay Whitsitt
Publisher : Unknown
Page : 200 pages
File Size : 55,5 Mb
Release : 1977
Category : Automatic data collection systems
ISBN : MINN:31951D03113547T

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Error Estimation and Separability Measures in Feature Selection for Multiclass Pattern Recognition by Stephen Jay Whitsitt Pdf

Author : Friedhelm Schwenker,Neamat El Gayar
Publisher : Springer
Page : 280 pages
File Size : 42,7 Mb
Release : 2010-04-16
Category : Computers
ISBN : 9783642121593

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Artificial Neural Networks in Pattern Recognition by Friedhelm Schwenker,Neamat El Gayar Pdf

Artificial Neural Networks in Pattern Recognition synthesizes the proceedings of the 4th IAPR TC3 Workshop, ANNPR 2010. Topics include supervised and unsupervised learning, feature selection, pattern recognition in signal and image processing.

Author : V. Cantoni,V. Di Gesu,S. Levialdi
Publisher : Springer Science & Business Media
Page : 506 pages
File Size : 55,8 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9781461310075

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Image Analysis and Processing II by V. Cantoni,V. Di Gesu,S. Levialdi Pdf

This book contains the proceedings of the 4th International Conference on Data Analysis and Processing held in Cefalu' (Palermo, ITALY) on September 23-25 1987. The aim of this Conference, now at its fourth edition, was to give a general view of the actual research in the area of methods and systems for achieving artificial vision as well as to have an up-dated information of the current activity in Europe. A number of invited speakers presented overviews of statistical classification problems and methods, non conventional archi tectures, mathematical morphology, robotic vision, analysis of range images in vision systems, pattern matching algorithms and astronomical data processing. Finally a survey of the discussion on the contribution of AI to Image Analysis is given. The papers presented at the Conference have been subdivided in four sections: knowledge based approaches, basic pattern recognition tools, multi features system based solutions, image analysis-applications. We must thank the IBM-Italia and the Digital Equipment Corpo ration for sponsoring this Conference. We feel that the days spent at Cefalu' were an important step toward the mutual exchange of scientific information within the image processing community. v. Cantoni Pavia University V. Di Gesu' Palermo University S. Levialdi Rome University v CONTENTS INVITED LECTURES . • • • • . • • • 3 Morphological Optics.