Pattern recognition
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-d...
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Amsterdam ; London :
Elsevier/Academic Press
c2009.
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| Edición: | 4th ed |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009628353106719 |
Tabla de Contenidos:
- Front Cover; Pattern Recognition; Copyright Page; Contents; Preface; Chapter 1 Introduction; 1.1 Is Pattern Recognition Important?; 1.2 Features, Feature Vectors, and Classifiers; 1.3 Supervised, Unsupervised, and Semi-Supervised Learning; 1.4 MATLAB Programs; 1.5 Outline of The Book; Chapter 2 Classifiers Based on Bayes Decision Theory; 2.1 Introduction; 2.2 Bayes Decision Theory; 2.3 Discriminant Functions and Decision Surfaces; 2.4 Bayesian Classification for Normal Distributions; 2.5 Estimation of Unknown Probability Density Functions; 2.6 The Nearest Neighbor Rule; 2.7 Bayesian Networks
- 2.8 Problems References; Chapter 3 Linear Classifiers; 3.1 Introduction; 3.2 Linear Discriminant Functions and Decision Hyperplanes; 3.3 The Perceptron Algorithm; 3.4 Least Squares Methods; 3.5 Mean Square Estimation Revisited; 3.6 Logistic Discrimination; 3.7 Support Vector Machines; 3.8 Problems; References; Chapter 4 Nonlinear Classifiers; 4.1 Introduction; 4.2 The XOR Problem; 4.3 The Two-Layer Perceptron; 4.4 Three-Layer Perceptrons; 4.5 Algorithms Based on Exact Classification of the Training Set; 4.6 The Back propagation Algorithm; 4.7 Variations on the Back propagation Theme
- 4.8 The Cost Function Choice 4.9 Choice of the Network Size; 4.10 A Simulation Example; 4.11 Networks with Weight Sharing; 4.12 Generalized Linear Classifiers; 4.13 Capacity of the l-Dimensional Space in Linear Dichotomies; 4.14 Polynomial Classifiers; 4.15 Radial Basis Function Networks; 4.16 Universal Approximators; 4.17 Probabilistic Neural Networks; 4.18 Support Vector Machines:The Nonlinear Case; 4.19 Beyond the SVM Paradigm; 4.20 Decision Trees; 4.21 Combining Classifiers; 4.22 The Boosting Approach to Combine Classifiers; 4.23 The Class Imbalance Problem; 4.24 Discussion; 4.25 Problems
- References Chapter 5 Feature Selection; 5.1 Introduction; 5.2 Preprocessing; 5.3 The Peaking Phenomenon; 5.4 Feature Selection Based on Statistical Hypothesis Testing; 5.5 The Receiver Operating Characteristics (ROC) Curve; 5.6 Class Separability Measures; 5.7 Feature Subset Selection; 5.8 Optimal Feature Generation; 5.9 Neural Networks and Feature Generation/Selection; 5.10 A Hint On Generalization Theory; 5.11 The Bayesian Information Criterion; 5.12 Problems; References; Chapter 6 Feature Generation I: Data Transformation and Dimensionality Reduction; 6.1 Introduction
- 6.2 Basis Vectors and Images 6.3 The Karhunen-Loève Transform; 6.4 The Singular Value Decomposition; 6.5 Independent Component Analysis; 6.6 Non-negative Matrix Factorization; 6.7 Nonlinear Dimensionality Reduction; 6.8 The Discrete Fourier Transform (DFT); 6.9 The Discrete Cosine and Sine Transforms; 6.10 The Hadamard Transform; 6.11 The Haar Transform; 6.12 The Haar Expansion Revisited; 6.13 Discrete Time Wavelet Transform (DTWT); 6.14 The Multi-resolution Interpretation; 6.15 Wavelet Packets; 6.16 A Look at Two-Dimensional Generalizations; 6.17 Applications; 6.18 Problems; References
- Chapter 7
