Concepts, Methods and Applications
Buch, Englisch, 318 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 522 g
ISBN: 978-3-642-62677-7
Verlag: Springer
The book provides a comprehensive view of pattern recognition concepts and methods, illustrated with real-life applications in several areas. A CD-ROM offered with the book includes datasets and software tools, making it easier to follow in a hands-on fashion, right from the start.
Zielgruppe
Graduate
Autoren/Hrsg.
Fachgebiete
- Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik
- Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizinische Fachgebiete Bildgebende Verfahren, Nuklearmedizin, Strahlentherapie Radiologie, Bildgebende Verfahren
- Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Signalverarbeitung
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision
Weitere Infos & Material
1 Basic Notions.- 1.1 Object Recognition.- 1.2 Pattern Similarity and PR Tasks.- 1.3 Classes, Patterns and Features.- 1.4 PR Approaches.- 1.5 PR Project.- 2 Pattern Discrimination.- 2.1 Decision Regions and Functions.- 2.2 Feature Space Metrics.- 2.3 The Covariance Matrix.- 2.4 Principal Components.- 2.5 Feature Assessment.- 2.6 The Dimensionality Ratio Problem.- Exercises.- 3 Data Clustering.- 3.1 Unsupervised Classification.- 3.2 The Standardization Issue.- 3.3 Tree Clustering.- 3.4 Dimensional Reduction.- 3.5 K-Means Clustering.- 3.6 Cluster Validation.- Exercises.- 4 Statistical Classification.- 4.1 Linear Discriminants.- 4.2 Bayesian Classification.- 4.3 Model-Free Techniques.- 4.4 Feature Selection.- 4.5 Classifier Evaluation.- 4.6 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.- 5.6 Performance of Neural Networks.- 5.7 Approximation Methods in NN Training.- 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.- 6.2 Structural Representations.- 6.3 Syntactic Analysis.- 6.4 Structural 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 StockExchange.- 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.
