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.

lgli/Z:\Bibliotik_\A Library\Introduction to Pattern Recognition - Wei Zhi.pdf

lgrsnf/Z:\Bibliotik_\A Library\Introduction to Pattern Recognition - Wei Zhi.pdf

nexusstc/Introduction to Pattern Recognition: A MATLAB Approach/65e9f018c864ffcbdd55271c2d6db5a1.pdf

zlib/Computers/Computer Science/Theodoridis, Sergios/Introduction to Pattern Recognition: A MATLAB Approach_10997675.pdf

Matlab Introduction to Pattern Recognition

Pattern recognition = Mo shi shi bie

Pattern Recognition Fourth Edition

Sergios Theodoridis; Aggelos Pikrakis; Konstantinos Koutroumbas; Dionisis Cavouras

Koutroumbas, Konstantinos, Theodoridis, Sergios

Konstantinos Koutroumbas; Sergios Theodoridis

Sergios Theodoridis ... [et al.]

Academic Press, Incorporated

Morgan Kaufmann Publishers

China Machine Press

Syngress Publishing

Elsevier Inc.

Brooks/Cole

Jing dian yuan ban shu ku, 4th ed., English photoprint ed, Beijing, China, 2009

4th ed., Amsterdam, London, Massachusetts, 2009

United States, United States of America

4th ed, Burlington, MA ; London, ©2009

Elsevier Ltd., Burlington, MA, 2009

Elsevier Ltd., Burlington, MA, 2010

Burlington, MA, Massachusetts, 2010

Fourth Edition, PT, 2008

Amsterdam, ©2010

London, ©2010

1, PS, 2010

4, 2008

lg2859054

producers:
Acrobat Distiller 8.1.0 (Windows)

{"edition":"4","isbns":["0123744865","1597492728","6520100064","9780123744869","9781597492720","9786520100062"],"last_page":219,"publisher":"Academic Press"}

"Compliment of the book Pattern recognition, 4th edition, by S. Theodoridis and K. Koutroumbas, Academic Press, 2009."
Includes bibliographical references and index.

Previous ed.: Amsterdam: Academic, 2003.
Includes bibliographical references and index.

MiU

Front Cover......Page 1
Title Page......Page 4
Copyright Page......Page 5
Table of Contents......Page 6
Preface......Page 10
1.2 Bayes Decision Theory......Page 14
1.3 The Gaussian Probability Density Function......Page 15
1.4.2 The Mahalanobis Distance Classifier......Page 19
1.4.3 Maximum Likelihood Parameter Estimation of Gaussian pdfs......Page 20
1.5 Mixture Models......Page 24
1.6 The Expectation-Maximization Algorithm......Page 26
1.7 Parzen Windows......Page 32
1.8 k-Nearest Neighbor Density Estimation......Page 34
1.9 The Naive Bayes Classifier......Page 35
1.10 The Nearest Neighbor Rule......Page 38
2.1 Introduction......Page 42
2.2 The Perceptron Algorithm......Page 43
2.2.1 The Online Form of the Perceptron Algorithm......Page 46
2.3 The Sum of Error Squares Classifier......Page 48
2.3.1 The Multiclass LS Classifier......Page 52
2.4 Support Vector Machines: The Linear Case......Page 56
2.4.1 Multiclass Generalizations......Page 61
2.5 SVM: The Nonlinear Case......Page 63
2.6 The Kernel Perceptron Algorithm......Page 71
2.7 The AdaBoost Algorithm......Page 76
2.8 Multilayer Perceptrons......Page 79
3.2 Principal Component Analysis......Page 92
3.3 The Singular Value Decomposition Method......Page 97
3.4 Fisher’s Linear Discriminant Analysis......Page 100
3.5 The Kernel PCA......Page 105
3.6 Laplacian Eigenmap......Page 114
4.2 Outlier Removal......Page 120
4.3 Data Normalization......Page 121
4.4 Hypothesis Testing: The t-Test......Page 124
4.5 The Receiver Operating Characteristic Curve......Page 126
4.6 Fisher’s Discriminant Ratio......Page 127
4.7 Class Separability Measures......Page 130
4.7.1 Divergence......Page 131
4.7.2 Bhattacharyya Distance and Chernoff Bound......Page 132
4.7.3 Measures Based on Scatter Matrices......Page 133
4.8 Feature Subset Selection......Page 135
4.8.1 Scalar Feature Selection......Page 136
4.8.2 Feature Vector Selection......Page 137
5.2 The Edit Distance......Page 150
5.3 Matching Sequences of Real Numbers......Page 152
5.4 Dynamic Time Warping in Speech Recognition......Page 156
6.2 Modeling......Page 160
6.3 Recognition and Training......Page 161
7.2 Basic Concepts and Definitions......Page 172
7.3 Clustering Algorithms......Page 173
7.4.1 BSAS Algorithm......Page 174
7.4.2 Clustering Refinement......Page 175
7.5.1 Hard Clustering Algorithms......Page 181
7.5.2 Nonhard Clustering Algorithms......Page 197
7.6 Miscellaneous Clustering Algorithms......Page 202
7.7 Hierarchical Clustering Algorithms......Page 211
7.7.1 Generalized Agglomerative Scheme......Page 212
7.7.2 Specific Agglomerative Clustering Algorithms......Page 213
7.7.3 Choosing the Best Clustering......Page 216
Appendix......Page 222
References......Page 228
Index......Page 230

<p>This book considers classical and current theory and practice, of supervised, unsupervised and semi-supervised pattern recognition, to build a complete background for professionals and students of engineering. The authors, leading experts in the field of pattern recognition, have provided an up-to-date, self-contained volume encapsulating this wide spectrum of information. The very latest methods are incorporated in this edition: semi-supervised learning, combining clustering algorithms, and relevance feedback. </p> <p>· Thoroughly developed to include many more worked examples to give greater understanding of the various methods and techniques</p> <p>· Many more diagrams included--now in two color--to provide greater insight through visual presentation</p> <ul> </ul> <p>· Matlab code of the most common methods are given at the end of each chapter.</p> <ul> </ul> <ul> </ul> <p>· More Matlab code is available, together with an accompanying manual, via this site </p> <ul> </ul> <p>· Latest hot topics included to further the reference value of the text including non-linear dimensionality reduction techniques, relevance feedback, semi-supervised learning, spectral clustering, combining clustering algorithms.</p> <p>· An accompanying book with Matlab code of the most common methods and algorithms in the book, together with a descriptive summary, and solved examples including real-life data sets in imaging, and audio recognition. The companion book will be available separately or at a special packaged price (ISBN: 9780123744869). </p><br><br><li>Thoroughly developed to include many more worked examples to give greater understanding of the various methods and techniques <li>Many more diagrams included--now in two color--to provide greater insight through visual presentation <li>Matlab code of the most common methods are given at the end of each chapter <li>An accompanying book with 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. The companion book is available separately or at a special packaged price (Book ISBN: 9780123744869. Package ISBN: 9780123744913) <li>Latest hot topics included to further the reference value of the text including non-linear dimensionality reduction techniques, relevance feedback, semi-supervised learning, spectral clustering, combining clustering algorithms <li>Solutions manual, powerpoint slides, and additional resources are available to faculty using the text for their course. Register at www.textbooks.elsevier.com and search on "Theodoridis" to access resources for instructor. </li>

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)

An accompanying manual to Theodoridis/Koutroumbas, Pattern Recognition, that 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.<br><br>*Matlab code and descriptive summary of the most common methods and algorithms in Theodoridis/Koutroumbas, Pattern Recognition 4e.<br>*Solved examples in Matlab, including real-life data sets in imaging and audio recognition<br>*Available separately or at a special package price with the main text (ISBN for package: 978-0-12-374491-3)

__Introduction to Pattern Recognition: A Matlab Approach__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.

"This book considers classical and current theory and practice of supervised, unsupervised and semi-supervised pattern recognition, to build a complete background for professionals and students of engineering. The authors have provided an up-to-date, self-contained volume encapsulating this wide spectrum of information. The very latest methods are incorporated in this edition including semi-supervised learning, non-linear dimensionality reduction techniques and spectral clustering."--Jacket

This is an accompanying manual to "Theodoridis/Koutroumbas, Pattern Recognition", that 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. It also includes Matlab code and descriptive summary of the most common methods and algorithms in "Theodoridis/Koutroumbas, Pattern Recognition 4e"

Classifiers based on Bayes decision theory
Classifiers based on cost function optimization
Data transformation : feature generation and dimensionality reduction
Feature selection
Template matching
Hidden Markov models
Clustering
Appendix.

2020-11-29