Pattern Recognition: Concepts, Methods and Applications


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.

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Paperback (Softcover reprint of the original 1st ed. 2001)
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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.

Read More Show Less

Editorial Reviews

From the Publisher

From the reviews of the first edition:

"The book gives an overview about the wide field of pattern recognition. … The book is primarily addressed to undergraduate and graduate students of engineering and computer science courses. It gives a good introduction into the field of clustering and pattern recognition." (Hans-Peter Altenburg, Zentralblatt MATH, Vol. 1009, 2003)

"‘Patern Recognition’ presents methods and techniques that are suitable for practical application in areas including robot assisted manufacture, medical diagnostic systems, forecast of economic variables, exploration of Earth’s resources, and satellite data analysis. … This book provides comprehensive, non-specialist coverage of pattern recognition. Although primarily aimed at undergraduate and graduate engineering and computer science students, its clear and practical coverage also makes it suitable for physicians, biologists, geologists and economists." (Assembly Automation, Vol. 22 (4), 2002)

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Product Details

  • ISBN-13: 9783642626777
  • Publisher: Springer Berlin Heidelberg
  • Publication date: 12/5/2012
  • Edition description: Softcover reprint of the original 1st ed. 2001
  • Pages: 318
  • Product dimensions: 6.14 (w) x 9.21 (h) x 0.72 (d)

Table of Contents

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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