Matlab Introduction to Pattern Recognition

A MATLAB Approach

By: Konstantinos Koutroumbas, Sergios Theodoridis

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Paperback | 17 March 2010 | Edition Number 1

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232 Pages

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23.5 x 19.05 x 1.25

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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)

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You Can Find This Book In

Non-Fiction Computing & I.T.Databases Data Capture & Analysis Engineering & Technology Electronics & Communications Engineering Communications Engineering & Telecommunications Computer Programming & Software Development Software Engineering Computer Science Artificial Intelligence Pattern Recognition

Graphical & Digital Media Applications 3D Graphics & Modelling Computer Vision Image Processing

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Hardcover

Published: 20th October 2008

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Preface	p. ix
Classifiers Based on Bayes Decision Theory	p. 1
Introduction	p. 1
Bayes Decision Theory	p. 1
The Gaussian Probability Density Function	p. 2
Minimum Distance Classifiers	p. 6
The Euclidean Distance Classifier	p. 6
The Mahalanobis Distance Classifier	p. 6
Maximum Likelihood Parameter Estimation of Gaussian pdfs	p. 7
Mixture Models	p. 11
The Expectation-Maximization Algorithm	p. 13
Parzen Windows	p. 19
k-Nearest Neighbor Density Estimation	p. 21
The Naive Bayes Classifier	p. 22
The Nearest Neighbor Rule	p. 25
Classifiers Based on Cost Function Optimization	p. 29
Introduction	p. 29
The Perceptron Algorithm	p. 30
The Online Form of the Perceptron Algorithm	p. 33
The Sum of Error Squares Classifier	p. 35
The Multiclass LS Classifier	p. 39
Support Vector Machines: The Linear Case	p. 43
Multiclass Generalizations	p. 48
SVM: The Nonlinear Case	p. 50
The Kernel Perceptron Algorithm	p. 58
The AdaBoost Algorithm	p. 63
Multilayer Perceptrons	p. 66
Data Transformation: Feature Generation and Dimensionality Reduction	p. 79
Introduction	p. 79
Principal Component Analysis	p. 79
The Singular Value Decomposition Method	p. 84
Fisher's Linear Discriminant Analysis	p. 87
The Kernel PCA	p. 92
Laplacian Eigenmap	p. 101
Feature Selection	p. 107
Introduction	p. 107
Outlier Removal	p. 107
Data Normalization	p. 108
Hypothesis Testing: The t-Test	p. 111
The Receiver Operating Characteristic Curve	p. 113
Fisher's Discriminant Ratio	p. 114
Class Separability Measures	p. 117
Divergence	p. 118
Bhattacharyya Distance and Chernoff Bound	p. 119
Measures Based on Scatter Matrices	p. 120
Feature Subset Selection	p. 122
Scalar Feature Selection	p. 123
Feature Vector Selection	p. 124
Template Matching	p. 137
Introduction	p. 137
The Edit Distance	p. 137
Matching Sequences of Real Numbers	p. 139
Dynamic Time Warping in Speech Recognition	p. 143
Hidden Markov Models	p. 147
Introduction	p. 147
Modeling	p. 147
Recognition and Training	p. 148
Clustering	p. 159
Introduction	p. 159
Basic Concepts and Definitions	p. 159
Clustering Algorithms	p. 160
Sequential Algorithms	p. 161
BSAS Algorithm	p. 161
Clustering Refinement	p. 162
Cost Function Optimization Clustering Algorithms	p. 168
Hard Clustering Algorithms	p. 168
Nonhard Clustering Algorithms	p. 184
Miscellaneous Clustering Algorithms	p. 189
Hierarchical Clustering Algorithms	p. 198
Generalized Agglomerative Scheme	p. 199
Specific Agglomerative Clustering Algorithms	p. 200
Choosing the Best Clustering	p. 203
Appendix	p. 209
References	p. 215
Index	p. 217
Table of Contents provided by Ingram. All Rights Reserved.

Matlab Introduction to Pattern Recognition

A MATLAB Approach

At a Glance

Paperback

Shipping

How to return your order

Defective items

You Can Find This Book In

Other Editions and Formats

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