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Introduction to Pattern Recognition: A Matlab Approach |  |
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Introduction to Pattern Recognition: A Matlab Approach | |
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.
目录
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
网友对Introduction to Pattern Recognition: A Matlab Approach的评论
The download links to the source code are no longer valid and there is no information online showing how to download the source code. This book is absolutely USELESS without the source code because most of the functions referred to have no source code printed in the book. The book just shows how to use the functions in examples so without the source code to download, the examples cannot even be run
UPDATE: The publisher informed me that they NO LONGER SUPPORT this book and any supplemental materials will no longer be supplied. Without the MATLAB code it is not possible to use this book!
This book was designed as an accompaniment to Pattern Recognition, Fourth Edition with additional intuitive descriptions of selected algorithms and Matlab-based problems with solutions.
This book is not a replacement for any pattern recognition book, because it lacks any real technical depth, but in conjunction with a complete text (I personally like this book's companion, also by Theodoridis) the content of this book can be put to good use for real-world applications.
Four stars because although the content is of high quality, the book sparsely covers the diverse subject of pattern classification. If a new edition is released with additional examples that more completely cover Theodoridis's text, that would easily be a 5-star book IMHO.
I have read the first chapter so far, however i have found that the book is so practical. I have passed machine learning in the school and i can say that this book a good snapshot of that subject. The most important thing is that the book includes lots of matlab codes which really help the readers understand the concept of the application of different classifiers in a practical way by solving vary applicable examples throughout the book.
I have the same problem with this book that I have with the main text - everything feels so rushed. It's a very good resource though. I'm glad I bought it. This text with real examples is a must if you've bought the main pattern recognition text by Theo.
This book was not required for my Pattern Recognition course but proved to be a beneficial companion to the Pattern Recognition textbook from the same authors. I referenced this book frequently throughout the class and a lot while designing our final assignment for the course.
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