Random Graphs for Statistical Pattern Recognition
Published on 1. February 2005
Software
Other digital
264 pages
978-0-471-72209-0 (ISBN)
Description
This book provides a comprehensive coverage of two timely fields, enhanced with many references and real-world examples. This valuable resource presents: a detailed look at the application of random graphs to pattern recognition; extensive examples of applications of the graphs studied, as well as the theoretical treatment of their properties; a unique compilation of new topics in discrete mathematics, pattern recognition, and machine learning; and, integrated discussions of CCCD with neighborhood graphs to the classification problem.
Reviews / Votes
"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
Language
English
Place of publication
New York
United States
Publishing group
John Wiley and Sons Ltd
Target group
Professional and scholarly
Weight
10 gr
ISBN-13
978-0-471-72209-0 (9780471722090)
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 C n,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.