Introduction to Pattern Recognition
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Introduction to Pattern Recognition : A Matlab Approach

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Description

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.
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Product details

  • Paperback | 231 pages
  • 187.96 x 231.14 x 12.7mm | 430.91g
  • Academic Press Inc
  • San Diego, United States
  • English
  • 0123744865
  • 9780123744869
  • 535,371

Table of contents

Preface

Chapter 1. Classifiers Based on Bayes Decision Theory

1.1 Introduction

1.2 Bayes Decision Theory

1.3 The Gaussian Probability Density Function

1.4 Minimum Distance Classifiers

1.4.1 The Euclidean Distance Classifier

1.4.2 The Mahalanobis Distance Classifier

1.4.3 Maximum Likelihood Parameter Estimation of Gaussian pdfs

1.5 Mixture Models

1.6 The Expectation-Maximization Algorithm

1.7 Parzen Windows

1.8 k-Nearest Neighbor Density Estimation

1.9 The Naive Bayes Classifier

1.10 The Nearest Neighbor Rule

Chapter 2. Classifiers Based on Cost Function Optimization

2.1 Introduction

2.2 The Perceptron Algorithm

2.2.1 The Online Form of the Perceptron Algorithm

2.3 The Sum of Error Squares Classifier

2.3.1 The Multiclass LS Classifier

2.4 Support Vector Machines: The Linear Case

2.4.1 Multiclass Generalizations

2.5 SVM: The Nonlinear Case

2.6 The Kernel Perceptron Algorithm

2.7 The AdaBoost Algorithm

2.8 Multilayer Perceptrons

Chapter 3. Data Transformation: Feature Generation and Dimensionality Reduction

3.1 Introduction

3.2 Principal Component Analysis

3.3 The Singular Value Decomposition Method

3.4 Fisher's Linear Discriminant Analysis

3.5 The Kernel PCA

3.6 Laplacian Eigenmap

Chapter 4. Feature Selection

4.1 Introduction

4.2 Outlier Removal

4.3 Data Normalization

4.4 Hypothesis Testing: The t-Test

4.5 The Receiver Operating Characteristic Curve

4.6 Fisher's Discriminant Ratio

4.7 Class Separability Measures

4.7.1 Divergence

4.7.2 Bhattacharyya Distance and Chernoff Bound

4.7.3 Measures Based on Scatter Matrices

4.8 Feature Subset Selection

4.8.1 Scalar Feature Selection

4.8.2 Feature Vector Selection

Chapter 5. Template Matching

5.1 Introduction

5.2 The Edit Distance

5.3 Matching Sequences of Real Numbers

5.4 Dynamic Time Warping in Speech Recognition

Chapter 6. Hidden Markov Models

6.1 Introduction

6.2 Modeling

6.3 Recognition and Training

Chapter 7. Clustering

7.1 Introduction

7.2 Basic Concepts and Definitions

7.3 Clustering Algorithms

7.4 Sequential Algorithms

7.4.1 BSAS Algorithm

7.4.2 Clustering Refinement

7.5 Cost Function Optimization Clustering Algorithms

7.5.1 Hard Clustering Algorithms

7.5.2 Nonhard Clustering Algorithms

7.6 Miscellaneous Clustering Algorithms

7.7 Hierarchical Clustering Algorithms

7.7.1 Generalized Agglomerative Scheme

7.7.2 Specific Agglomerative Clustering Algorithms

7.7.3 Choosing the Best Clustering

Appendix

References

Index
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About Dionisis Cavouras

Sergios Theodoridis is Professor of Signal Processing and Machine Learning in the Department of Informatics and Telecommunications of the University of Athens. He is the co-author of the bestselling book, Pattern Recognition, and the co-author of Introduction to Pattern Recognition: A MATLAB Approach. He serves as Editor-in-Chief for the IEEE Transactions on Signal Processing, and he is the co-Editor in Chief with Rama Chellapa for the Academic Press Library in Signal Processing. He has received a number of awards including the 2014 IEEE Signal Processing Magazine Best Paper Award, the 2009 IEEE Computational Intelligence Society Transactions on Neural Networks Outstanding Paper Award, the 2014 IEEE Signal Processing Society Education Award, the EURASIP 2014 Meritorious Service Award, and he has served as a Distinguished Lecturer for the IEEE Signal Processing Society and the IEEE Circuits and Systems Society. He is a Fellow of EURASIP and a Fellow of IEEE. Aggelos Pikrakis is a Lecturer in the Department of Informatics at the University of Piraeus. His research interests stem from the fields of pattern recognition, audio and image processing, and music information retrieval. He is also the co-author of Introduction to Pattern Recognition: A MATLAB Approach (Academic Press, 2010). Konstantinos Koutroumbas acquired a degree from the University of Patras, Greece in Computer Engineering and Informatics in 1989, a MSc in Computer Science from the University of London, UK in 1990, and a Ph.D. degree from the University of Athens in 1995. Since 2001 he has been with the Institute for Space Applications and Remote Sensing of the National Observatory of Athens.
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