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Autor: David J. Marchette
ISBN-13: 9780471722083
Einband: E-Book
Seiten: 264
Sprache: Englisch
eBook Typ: PDF
eBook Format: E-Book
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Random Graphs for Statistical Pattern Recognition

Wiley Series in Probability and Statistics
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A timely convergence of two widely used disciplines
Random Graphs for Statistical Pattern Recognition is the firstbook to address the topic of random graphs as it applies tostatistical pattern recognition. Both topics are of vital interestto researchers in various mathematical and statistical fields andhave never before been treated together in one book. The use ofdata random graphs in pattern recognition in clustering andclassification is discussed, and the applications for bothdisciplines are enhanced with new tools for the statistical patternrecognition community. New and interesting applications for randomgraph users are also introduced.

This important addition to statistical literaturefeatures:
* Information that previously has been available only throughscattered journal articles
* Practical tools and techniques for a wide range of real-worldapplications
* New perspectives on the relationship between patternrecognition and computational geometry
* Numerous experimental problems to encourage practicalapplications

With its comprehensive coverage of two timely fields, enhancedwith many references and real-world examples, Random Graphs forStatistical Pattern Recognition is a valuable resource forindustry professionals and students alike.
Preface.

Acknowledgments.

1. Preliminaries.

1.1 Graphs and Digraphs.

1.2 Statistical Pattern Recognition.

1.3 Statistical Issues.

1.4 Applications.

1.5 Further Reading.

2. Computational Geometry.

2.1 Introduction.

2.2 Voronoi Cells and Delaunay Triangularization.

2.3 Alpha Hulls.

2.4 Minimum Spanning Trees.

2.5 Further Reading.

3. Neighborhood Graphs.

3.1 Introduction.

3.2 Nearest-Neighbor Graphs.

3.3 k-Nearest Neighbor Graphs.

3.4 Relative Neighborhood Graphs.

3.5 Gabriel Graphs.

3.6 Application: Nearest Neighbor Prototypes.

3.7 Sphere of Influence Graphs.

3.8 Other Relatives.

3.9 Asymptotics.

3.10 Further Reading.

4. Class Cover Catch Digraphs.

4.1 Catch Digraphs.

4.2 Class Covers.

4.3 Dominating Sets.

4.4 Distributional Results for Cn,m-graphs.

4.5 Characterizations.

4.6 Scale Dimension.

4.7 (alpha,beta) Graphs

4.8 CCCD Classification.

4.9 Homogeneous CCCDs.

4.10 Vector Quantization.

4.11 Random Walk Version.

4.12 Further Reading.

5. Cluster Catch Digraphs.

5.1 Basic Definitions.

5.2 Dominating Sets.

5.3 Connected Components.

5.4 Variable Metric Clustering.

6. Computational Methods.

6.1 Introduction.

6.2 Kd-Trees.

6.3 Class Cover Catch Digraphs.

6.4 Cluster Catch Digraphs.

6.5 Voroni Regions and Delaunay Triangularizations.

6.6 Further Reading.

References.

Author Index.

Subject Index.

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Autor: David J. Marchette
ISBN-13 :: 9780471722083
ISBN: 0471722081
Verlag: John Wiley & Sons
Seiten: 264
Sprache: Englisch
Auflage 1. Auflage
Sonstiges: Ebook