A timely convergence of two widely used disciplines
Random Graphs for Statistical Pattern Recognition is the first
book to address the topic of random graphs as it applies to
statistical pattern recognition. Both topics are of vital interest
to researchers in various mathematical and statistical fields and
have never before been treated together in one book. The use of
data random graphs in pattern recognition in clustering and
classification is discussed, and the applications for both
disciplines are enhanced with new tools for the statistical pattern
recognition community. New and interesting applications for random
graph users are also introduced.
This important addition to statistical literature
features:
* Information that previously has been available only through
scattered journal articles
* Practical tools and techniques for a wide range of real-world
applications
* New perspectives on the relationship between pattern
recognition and computational geometry
* Numerous experimental problems to encourage practical
applications
With its comprehensive coverage of two timely fields, enhanced
with many references and real-world examples, Random Graphs for
Statistical Pattern Recognition is a valuable resource for
industry professionals and students alike.
Cuprins
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.
Despre autor
DAVID J. MARCHETTE, Ph D, is a researcher at the Naval Surface Warfare Center in Dahlgren, Virginia, where he investigates computational statistics and pattern recognition, primarily as it applies to image processing, automatic target recognition, and computer security. He is also an adjunct professor at George Mason University and a lecturer at Johns Hopkins University.
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