Pattern Recognition: Concepts, Methods and Applications - Tapa blanda

Sinopsis

1 Basic Notions.- 1.1 Object Recognition.- 1.2 Pattern Similarity and PR Tasks.- 1.2.1 Classification Tasks.- 1.2.2 Regression Tasks.- 1.2.3 Description Tasks.- 1.3 Classes, Patterns and Features.- 1.4 PR Approaches.- 1.4.1 Data Clustering.- 1.4.2 Statistical Classification.- 1.4.3 Neural Networks.- 1.4.4 Structural PR.- 1.5 PR Project.- 1.5.1 Project Tasks.- 1.5.2 Training and Testing.- 1.5.3 PR Software.- 2 Pattern Discrimination.- 2.1 Decision Regions and Functions.- 2.1.1 Generalized Decision Functions.- 2.1.2 Hyperplane Separability.- 2.2 Feature Space Metrics.- 2.3 The Covariance Matrix.- 2.4 Principal Components.- 2.5 Feature Assessment.- 2.5.1 Graphic Inspection.- 2.5.2 Distribution Model Assessment.- 2.5.3 Statistical Inference Tests.- 2.6 The Dimensionality Ratio Problem.- Exercises.- 3 Data Clustering.- 3.1 Unsupervised Classification.- 3.2 The Standardization Issue.- 3.3 Tree Clustering.- 3.3.1 Linkage Rules.- 3.3.2 Tree Clustering Experiments.- 3.4 Dimensional Reduction.- 3.5 K-Means Clustering.- 3.6 Cluster Validation.- Exercises.- 4 Statistical Classification.- 4.1 Linear Discriminants.- 4.1.1 Minimum Distance Classifier.- 4.1.2 Euclidian Linear Discriminants.- 4.1.3 Mahalanobis Linear Discriminants.- 4.1.4 Fisher's Linear Discriminant.- 4.2 Bayesian Classification.- 4.2.1 Bayes Rule for Minimum Risk.- 4.2.2 Normal Bayesian Classification.- 4.2.3 Reject Region.- 4.2.4 Dimensionality Ratio and Error Estimation.- 4.3 Model-Free Techniques.- 4.3.1 The Parzen Window Method.- 4.3.2 The K-Nearest Neighbours Method.- 4.3.3 The ROC Curve.- 4.4 Feature Selection.- 4.5 Classifier Evaluation.- 4.6 Tree Classifiers.- 4.6.1 Decision Trees and Tables.- 4.6.2 Automatic Generation of Tree Classifiers.- 4.7 Statistical Classifiers in Data Mining.- Exercises.- 5 Neural Networks.- 5.1 LMS Adjusted Discriminants.- 5.2 Activation Functions.- 5.3 The Perceptron Concept.- 5.4 Neural Network Types.- 5.5 Multi-Layer Perceptrons.- 5.5.1 The Back-Propagation Algorithm.- 5.5.2 Practical aspects.- 5.5.3 Time Series.- 5.6 Performance of Neural Networks.- 5.6.1 Error Measures.- 5.6.2 The Hessian Matrix.- 5.6.3 Bias and Variance in NN Design.- 5.6.4 Network Complexity.- 5.6.5 Risk Minimization.- 5.7 Approximation Methods in NN Training.- 5.7.1 The Conjugate-Gradient Method.- 5.7.2 The Levenberg-Marquardt Method.- 5.8 Genetic Algorithms in NN Training.- 5.9 Radial Basis Functions.- 5.10 Support Vector Machines.- 5.11 Kohonen Networks.- 5.12 Hopfield Networks.- 5.13 Modular Neural Networks.- 5.14 Neural Networks in Data Mining.- Exercises.- 6 Structural Pattern Recognition.- 6.1 Pattern Primitives.- 6.1.1 Signal Primitives.- 6.1.2 Image Primitives.- 6.2 Structural Representations.- 6.2.1 Strings.- 6.2.2 Graphs.- 6.2.3 Trees.- 6.3 Syntactic Analysis.- 6.3.1 String Grammars.- 6.3.2 Picture Description Language.- 6.3.3 Grammar Types.- 6.3.4 Finite-State Automata.- 6.3.5 Attributed Grammars.- 6.3.6 Stochastic Grammars.- 6.3.7 Grammatical Inference.- 6.4 Structural Matching.- 6.4.1 String Matching.- 6.4.2 Probabilistic Relaxation Matching.- 6.4.3 Discrete Relaxation Matching.- 6.4.4 Relaxation Using Hopfield Networks.- 6.4.5 Graph and Tree Matching.- Exercises.- Appendix A-CD Datasets.- A.1 Breast Tissue.- A.2 Clusters.- A.3 Cork Stoppers.- A.4 Crimes.- A.5 Cardiotocographic Data.- A.6 Electrocardiograms.- A.7 Foetal Heart Rate Signals.- A.8 FHR-Apgar.- A.9 Firms.- A.10 Foetal Weight.- A.11 Food.- A.12 Fruits.- A.13 Impulses on Noise.- A.14 MLP Sets.- A.15 Norm2c2d.- A.16 Rocks.- A.17 Stock Exchange.- A.18 Tanks.- A.19 Weather.- Appendix B-CD Tools.- B.1 Adaptive Filtering.- B.2 Density Estimation.- B.3 Design Set Size.- B.4 Error Energy.- B.5 Genetic Neural Networks.- B.6 Hopfield network.- B.7 k-NN Bounds.- B.8 k-NN Classification.- B.9 Perceptron.- B.10 Syntactic Analysis.- Appendix C-Orthonormal Transformation.- Appendix C-Orthonormal Transformation.

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