%0 Generic

%T Iintroduction to pattern recognition a MATLAB approach

%A Theodoridis, Sergios

%A Koutroumbas, Konstantinos

%I Elsevier Academic Press

%@ 0123744865

%@ 9780123744869

%@ 1282618261

%@ 9781282618268

%K MATLAB

%K Pattern recognition systems

%K Pattern perception

%K Pattern Recognition, Automated

%K Online-Ressource

%K Perception des structures

%K Reconnaissance des formes (Informatique)

%K Electronic books

%K Mustererkennung

%D c2010

%X Includes bibliographical references and index. - Description based on print version record

%X Front Cover; Title Page; Copyright Page; 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

%X Chapter 2. Classifiers Based on Cost Function Optimization2.1 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

%X 3.3 The Singular Value Decomposition Method3.4 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

%X Chapter 5. Template Matching5.1 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

%X 7.7 Hierarchical Clustering Algorithms7.7.1 Generalized Agglomerative Scheme; 7.7.2 Specific Agglomerative Clustering Algorithms; 7.7.3 Choosing the Best Clustering; Appendix; References; Index

%C Elsevier Academic Press

%C Amsterdam

%U http://slubdd.de/katalog?TN_libero_mab2