Random Graphs for Statistical Pattern Recognition
1st Edition
Published on 12. March 2004
Book
Hardback
264 pages
978-0-471-22176-0 (ISBN)
Description
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.
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.
Reviews / Votes
"...constructed...as a book on random graphs, this is quite a good one." (Journal of the American Statistical Association, September 2006) "...I recommend this book to those who...wish to explore the exciting place where graph theory and pattern recognition meet." (Statistics in Medical Research, October 2005)"This well-written book presents practical tools, and information that was previously found scattered in various journals." (Computing Reviews.com, March 9, 2005)
"...an excellent resource book that would be a valuable addition..." (Technometrics, February 2005)
"...clearly and accessible written, and nicely conveys the power, breadth and applicability of some very elegant ideas..." (Short Book Reviews, Vol.24, No.3, December 2004)
"Buy this book if use graphs in cluster and classification analysis." (Journal of Classification, Vol.21, No.2, 2004)
More details
Series
Language
English
Place of publication
United States
Publishing group
John Wiley & Sons Inc
Target group
Professional and scholarly
Illustrations
Charts: 5 B&W, 0 Color; Photos: 7 B&W, 0 Color; Drawings: 24 B&W, 0 Color; Graphs: 86 B&W, 0 Color
Dimensions
Height: 249 mm
Width: 162 mm
Thickness: 23 mm
Weight
572 gr
ISBN-13
978-0-471-22176-0 (9780471221760)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Person
DAVID J. MARCHETTE, PhD, 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.
Content
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 (?,?) 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.
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 (?,?) 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.