Pattern Recognition and Classification (Record no. 95379)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 05691nam a22004935i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-1-4614-5323-9 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | DE-He213 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20140220082819.0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION | |
| fixed length control field | cr nn 008mamaa |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 121026s2013 xxu| s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781461453239 |
| -- | 978-1-4614-5323-9 |
| 024 7# - OTHER STANDARD IDENTIFIER | |
| Standard number or code | 10.1007/978-1-4614-5323-9 |
| Source of number or code | doi |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | Q337.5 |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | TK7882.P3 |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | UYQP |
| Source | bicssc |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | COM016000 |
| Source | bisacsh |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.4 |
| Edition number | 23 |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Dougherty, Geoff. |
| Relator term | author. |
| 245 10 - TITLE STATEMENT | |
| Title | Pattern Recognition and Classification |
| Medium | [electronic resource] : |
| Remainder of title | An Introduction / |
| Statement of responsibility, etc | by Geoff Dougherty. |
| 264 #1 - | |
| -- | New York, NY : |
| -- | Springer New York : |
| -- | Imprint: Springer, |
| -- | 2013. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | XI, 196 p. 158 illus., 104 illus. in color. |
| Other physical details | online resource. |
| 336 ## - | |
| -- | text |
| -- | txt |
| -- | rdacontent |
| 337 ## - | |
| -- | computer |
| -- | c |
| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
| -- | cr |
| -- | rdacarrier |
| 347 ## - | |
| -- | text file |
| -- | |
| -- | rda |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 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 ## - SUMMARY, ETC. | |
| Summary, etc | 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 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Computer science. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Optical pattern recognition. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Biology |
| General subdivision | Data processing. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Algorithms. |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Computer Science. |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Pattern Recognition. |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Nonlinear Dynamics. |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Signal, Image and Speech Processing. |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Computer Appl. in Life Sciences. |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Algorithms. |
| 710 2# - ADDED ENTRY--CORPORATE NAME | |
| Corporate name or jurisdiction name as entry element | SpringerLink (Online service) |
| 773 0# - HOST ITEM ENTRY | |
| Title | Springer eBooks |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Display text | Printed edition: |
| International Standard Book Number | 9781461453222 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | http://dx.doi.org/10.1007/978-1-4614-5323-9 |
| 912 ## - | |
| -- | ZDB-2-SCS |
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