Pattern Recognition

Pattern recognition currently comprises a vast body of methods supporting the development of numerous applications in many different areas of activity. The generally recognized relevance of pattern recognition methods and techniques lies, for the most part, in the general trend of "intelligent" task emulation, which has definitely pervaded our daily life. Robot assisted manufacture, medical diagnostic systems, forecast of economic variables, exploration of Earth's resources, and analysis of satellite data are just a few examples of activity fields where this trend applies. The pervasiveness of pattern recognition has boosted the number of task specific methodologies and enriched the number of links with other disciplines. As counterbalance to this dispersive tendency there have been, more recently, new theoretical developments that are bridging together many of the classical pattern recognition methods and presenting a new perspective of their links and inner workings. This book has its origin in an introductory course on pattern recognition taught at the Electrical and Computer Engineering Department, Oporto University. From the initial core of this course, the book grew with the intent of presenting a comprehensive and articulated view of pattern recognition methods combined with the intent of clarifying practical issues with the aid of examples and applications to real-life data. The book is primarily addressed to undergraduate and graduate students attending pattern recognition courses of engineering and computer science curricula.

Unifying approach describing all relevant and recent methods Real-life problems in engineering, medicine, economy, geology solved in detail Guidance how to design PR systems and use software tools in practical applications Mathematics made easy CD-ROM with solved exercises and important software tools Includes supplementary material: sn.pub/extras

Klappentext
The book provides a comprehensive view of Pattern Recognition concepts and methods, illustrated with real-life applications in several areas (e.g. engineering, medicine, economy, geology). It is appropriate as a textbook of Pattern Recognition courses and also for professionals and researchers who need to apply Pattern Recognition techniques. These are explained in a unified and innovative way, with multiple examples enhancing the clarification of concepts and the application of methods. Recent results in the main Pattern Recognition areas (Statistical Classification, Neural Networks and Structural Recognition) are presented. A CD offered with the book includes datasets and software tools, making it easier for the reader to follow the taught matters in a hands-on fashion right from the start.

Inhalt
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 ACD 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 BCD 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 COrthonormal Transformation.- Appendix COrthonormal Transformation.