Pattern Recognition and Classification An Introduction /

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 Classificati...

Full description

Bibliographic Details
Published in:Springer eBooks
Main Author: Dougherty, Geoff (Author)
Corporate Author: SpringerLink (Online service)
Format: eBook
Language:English
Published: New York, NY : Springer New York : Imprint: Springer, 2013.
Subjects:
Online Access:http://dx.doi.org/10.1007/978-1-4614-5323-9
Перейти в каталог НБ ТГУ
LEADER 05998nam a22005175i 4500
001 vtls000483692
003 RU-ToGU
005 20210922065555.0
007 cr nn 008mamaa
008 140715s2013 xxu| s |||| 0|eng d
020 |a 9781461453239  |9 978-1-4614-5323-9 
024 7 |a 10.1007/978-1-4614-5323-9  |2 doi 
035 |a to000483692 
039 9 |y 201407151936  |z Александр Эльверович Гилязов 
040 |a Springer  |c Springer  |d RU-ToGU 
050 4 |a Q337.5 
050 4 |a TK7882.P3 
072 7 |a UYQP  |2 bicssc 
072 7 |a COM016000  |2 bisacsh 
082 0 4 |a 006.4  |2 23 
100 1 |a Dougherty, Geoff.  |e author.  |9 413350 
245 1 0 |a Pattern Recognition and Classification  |h [electronic resource] :  |b An Introduction /  |c by Geoff Dougherty. 
260 |a New York, NY :  |b Springer New York :  |b Imprint: Springer,  |c 2013.  |9 724206 
300 |a XI, 196 p. 158 illus., 104 illus. in color.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
505 0 |a Preface -- Acknowledgments -- Chapter 1 Introduction -- 1.1 Overview -- 1.2 Classification -- 1.3 Organization of the Book -- Bibliography -- Exercises -- Chapter 2 Classification -- 2.1 The Classification Process -- 2.2 Features -- 2.3 Training and Learning -- 2.4 Supervised Learning and Algorithm Selection -- 2.5 Approaches to Classification -- 2.6 Examples -- 2.6.1 Classification by Shape -- 2.6.2 Classification by Size -- 2.6.3 More Examples -- 2.6.4 Classification of Letters -- Bibliography -- Exercises -- Chapter 3 Non-Metric Methods -- 3.1 Introduction -- 3.2 Decision Tree Classifier -- 3.2.1 Information, Entropy and Impurity -- 3.2.2 Information Gain -- 3.2.3 Decision Tree Issues -- 3.2.4 Strengths and Weaknesses -- 3.3 Rule-Based Classifier -- 3.4 Other Methods -- Bibliography -- Exercises -- Chapter 4 Statistical Pattern Recognition -- 4.1 Measured Data and Measurement Errors -- 4.2 Probability Theory -- 4.2.1 Simple Probability Theory -- 4.2.2 Conditional Probability and Bayes' Rule -- 4.2.3 Naïve Bayes classifier -- 4.3 Continuous Random Variables -- 4.3.1 The Multivariate Gaussian -- 4.3.2 The Covariance Matrix -- 4.3.3 The Mahalanobis Distance -- Bibliography -- Exercises -- Chapter 5 Supervised Learning -- 5.1 Parametric and Non-Parametric Learning -- 5.2 Parametric Learning -- 5.2.1 Bayesian Decision Theory -- 5.2.2 Discriminant Functions and Decision Boundaries -- 5.2.3 MAP (Maximum A Posteriori) Estimator -- Bibliography -- Exercises -- Chapter 6 Non-Parametric Learning -- 6.1 Histogram Estimator and Parzen Windows -- 6.2 k-Nearest Neighbor (k-NN) Classification -- 6.3 Artificial Neural Networks (ANNs) -- 6.4 Kernel Machines -- Bibliography -- Exercises -- Chapter 7 Feature Extraction and Selection -- 7.1 Reducing Dimensionality -- 7.1.1 Pre-Processing -- 7.2 Feature Selection -- 7.2.1 Inter/Intra-Class Distance -- 7.2.2 Subset Selection -- 7.3 Feature Extraction -- 7.3.1 Principal Component Analysis (PCA) -- 7.3.2 Linear Discriminant Analysis (LDA) -- Bibliography -- Exercises -- Chapter 8 Unsupervised Learning -- 8.1 Clustering -- 8.2 k-Means Clustering -- 8.2.1 Fuzzy c-Means Clustering -- 8.3 (Agglomerative) Hierarchical Clustering -- Bibliography -- Exercises -- Chapter 9 Estimating and Comparing Classifiers -- 9.1 Comparing Classifiers and the No Free Lunch Theorem -- 9.1.2 Bias and Variance -- 9.2 Cross-Validation and Resampling Methods -- 9.2.1 The Holdout Method -- 9.2.2 k-Fold Cross-Validation -- 9.2.3 Bootstrap -- 9.3 Measuring Classifier Performance   -- 9.4 Comparing Classifiers -- 9.4.1 ROC curves -- 9.4.2 McNemar's Test -- 9.4.3 Other Statistical Tests -- 9.4.4 The Classification Toolbox -- 9.5 Combining classifiers -- Bibliography -- Chapter 10 Projects -- 10.1 Retinal Tortuosity as an Indicator of Disease -- 10.2 Segmentation by Texture -- 10.3 Biometric Systems -- 10.3.1 Fingerprint Recognition -- 10.3.2 Face Recognition -- Bibliography -- Index. 
520 |a 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 estimating classifier performance and combining classifiers, and details of particular project applications are addressed in the later chapters. This book is suitable for undergraduates and graduates studying pattern recognition and machine learning. 
650 0 |a Computer Science.  |9 155490 
650 0 |a Optical pattern recognition.  |9 304126 
650 0 |a Biology  |x Data processing.  |9 305000 
650 0 |a Algorithms.  |9 304813 
650 1 4 |a Computer Science.  |9 155490 
650 2 4 |a Pattern Recognition.  |9 304129 
650 2 4 |a Nonlinear Dynamics.  |9 410417 
650 2 4 |a Signal, Image and Speech Processing.  |9 274103 
650 2 4 |a Computer Appl. in Life Sciences.  |9 305001 
650 2 4 |a Algorithms.  |9 304813 
710 2 |a SpringerLink (Online service)  |9 143950 
773 0 |t Springer eBooks 
856 4 0 |u http://dx.doi.org/10.1007/978-1-4614-5323-9 
856 |y Перейти в каталог НБ ТГУ  |u https://koha.lib.tsu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=356076 
912 |a ZDB-2-SCS 
950 |a Computer Science (Springer-11645) 
999 |c 356076  |d 356076