David J Marchette

Random Graphs for Statistical Pattern Recognition

• Gebundenes Buch

Produktbeschreibung

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 Integrated discussions of CCCD with neighborhood graphs to the classification problem

Produktdetails

• Verlag: Wiley
• Seitenzahl: 264
• Erscheinungstermin: 23. Februar 2004
• Englisch
• Abmessung: 241mm x 154mm x 24mm
• Gewicht: 572g
• ISBN-13: 9780471221760
• ISBN-10: 0471221767
• Artikelnr.: 21290790

Autorenporträt

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.

Inhaltsangabe

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.

Rezensionen

"...constructed...as a book on random graphs, this is quitea good one." (Journal of the American StatisticalAssociation, September 2006)

"...I recommend this book to those who...wish to explore theexciting place where graph theory and pattern recognition meet."(Statistics in Medical Research, October 2005)

"This well-written book presents practical tools, andinformation that was previously found scattered in variousjournals." (Computing Reviews.com, March 9, 2005)

"...an excellent resource book that would be a valuableaddition..." (Technometrics, February 2005)

"...clearly and accessible written, and nicely conveysthe power, breadth and applicability of some very elegantideas..." (Short Book Reviews, Vol.24, No.3, December2004)

"Buy this book if use graphs in cluster and classificationanalysis." (Journal of Classification, Vol.21, No.2,2004)