Pattern Recognition, Fourth Edition
- 17h 30m
- Konstantinos Koutroumbas, Sergios Theodoridis
- Elsevier Science and Technology Books, Inc.
- 2009
This book considers classical and current theory and practice, of supervised, unsupervised and semi-supervised pattern recognition, to build a complete background for professionals and students of engineering. The authors, leading experts in the field of pattern recognition, have provided an up-to-date, self-contained volume encapsulating this wide spectrum of information. The very latest methods are incorporated in this edition: semi-supervised learning, combining clustering algorithms, and relevance feedback.
- Thoroughly developed to include many more worked examples to give greater understanding of the various methods and techniques
- Many more diagrams included--now in two color--to provide greater insight through visual presentation
- Matlab code of the most common methods are given at the end of each chapter
- Latest hot topics included to further the reference value of the text including non-linear dimensionality reduction techniques, relevance feedback, semi-supervised learning, spectral clustering, combining clustering algorithms
In this Book
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Introduction
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Classifiers Based on Bayes Decision Theory
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Linear Classifiers
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Nonlinear Classifiers
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Feature Selection
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Feature Generation I—Data Transformation and Dimensionality Reduction
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Feature Generation II
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Template Matching
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Context-Dependent Classification
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Supervised Learning—The Epilogue
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Clustering—Basic Concepts
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Clustering Algorithms I—Sequential Algorithms
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Clustering Algorithms II—Hierarchical Algorithms
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Clustering Algorithms III—Schemes Based on Function Optimization
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Clustering Algorithms IV
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Cluster Validity
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