LEADER 01158nam0-2200361---450 001 990008606540403321 005 20190520104900.0 010 $a0-85199-058-4 035 $a000860654 035 $aFED01000860654 035 $a(Aleph)000860654FED01 035 $a000860654 100 $a20080128d2006----km-y0itay50------ba 101 0 $aeng 102 $aGB 105 $aa-------001yy 200 1 $aEnvironmental impact of invertebrates for biological control of arthropods$emethods and risk assessment$fedited by Franz Bigler, Dirk Babendreier, Ulrich Kuhlmann 210 $aWallingford ; Cambridge$cCABI Publishing$d2006 215 $aXV, 299 p.$cill.$d26 cm 452 0$1001000988549 610 0 $aControllo biologico 610 0 $aAntiparassitari 676 $a632.96$v20$zita 702 1$aBigler,$bFranz 702 1$aBabendreier,$bDirk 702 1$aKuhlmann,$bUlrich 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990008606540403321 952 $a60 632.96 BIGF 2006$b11104$fFAGBC 959 $aFAGBC 996 $aEnvironmental impact of invertebrates for biological control of arthropods$9712632 997 $aUNINA LEADER 04763nam 22006494a 450 001 9910830590103321 005 20230617031345.0 010 $a1-280-27535-9 010 $a9786610275359 010 $a0-470-34946-8 010 $a0-471-72208-1 010 $a0-471-72209-X 035 $a(CKB)1000000000018950 035 $a(EBL)225801 035 $a(OCoLC)475932211 035 $a(SSID)ssj0000232529 035 $a(PQKBManifestationID)11220035 035 $a(PQKBTitleCode)TC0000232529 035 $a(PQKBWorkID)10210490 035 $a(PQKB)11319215 035 $a(MiAaPQ)EBC225801 035 $a(EXLCZ)991000000000018950 100 $a20031107d2004 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aRandom graphs for statistical pattern recognition$b[electronic resource] /$fDavid J. Marchette 210 $aHoboken, N.J. $cWiley-Interscience$dc2004 215 $a1 online resource (261 p.) 225 1 $aWiley series in probability and statistics 300 $aDescription based upon print version of record. 311 $a0-471-22176-7 320 $aIncludes bibliographical references (p. 213-227) and indexes. 327 $aRandom Graphs for Statistical Pattern Recognition; Contents; Preface; Acknowledgments; 1 Preliminaries; 1.1 Graphs and Digraphs; 1.1.1 Graphs; 1.1.2 Digraphs; 1.1.3 Random Graphs; 1.2 Statistical Pattern Recognition; 1.2.1 Classification; 1.2.2 Curse of Dimensionality; 1.2.3 Clustering; 1.3 Statistical Issues; 1.4 Applications; 1.4.1 Artificial Nose; 1.4.2 Hyperspectral Image; 1.4.3 Gene Expression; 1.5 Further Reading; 2 Computational Geometry; 2.1 Introduction; 2.2 Voronoi Cells and Delaunay Triangularization; 2.2.1 Poisson Voronoi Cells; 2.3 Alpha Hulls; 2.4 Minimum Spanning Trees 327 $a2.4.1 Alpha Hulls and the MST2.4.2 Clustering; 2.4.3 Classification Complexity; 2.4.4 Application: Renyi Divergence; 2.4.5 Application: Image Segmentation; 2.5 Further Reading; 3 Neighborhood Graphs; 3.1 Introduction; 3.1.1 Application: Image Processing; 3.2 Nearest-Neighbor Graphs; 3.3 k-Nearest-Neighbor Graphs; 3.3.1 Application: Measures of Association; 3.3.2 Application: Artificial Nose; 3.3.3 Application: Outlier Detection; 3.3.4 Application: Dimensionality Reduction; 3.4 Relative Neighborhood Graphs; 3.5 Gabriel Graphs; 3.5.1 Gabriel Graphs and Alpha Hulls 327 $a3.5.2 Application: Nearest-Neighbor Prototypes3.6 Sphere-of-Influence Graphs; 3.7 Sphere-of-Attraction Graphs; 3.8 Other Relatives; 3.9 Asymptotics; 3.10 Further Reading; 4 Class Cover Catch Digraphs; 4.1 Catch Digraphs; 4.1.1 Sphere Digraphs; 4.2 Class Covers; 4.2.1 Basic Definitions; 4.3 Dominating sets; 4.4 Distributional Results for Cn,m-graphs; 4.4.1 Univariate Case; 4.4.2 Multivariate CCCDs; 4.5 Characterizations; 4.6 Scale Dimension; 4.6.1 Application: Latent Class Discovery; 4.7 (a,b) Graphs; 4.8 CCCD Classification; 4.9 Homogeneous CCCDs; 4.10 Vector Quantization 327 $a4.11 Random Walk Version4.11.1 Application: Face Detection; 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; 6.2.1 Data Structure; 6.2.2 Building the Tree; 6.2.3 Searching the Tree; 6.3 Class Cover Catch Digraphs; 6.4 Cluster Catch Digraphs; 6.5 Voronoi Regions and Delaunay Triangularizations; 6.6 Further Reading; References; Author Index; Subject Index 330 $aA timely convergence of two widely used disciplines Random Graphs for Statistical Pattern Recognition is the first book to address the topic of random graphs as it applies to statistical pattern recognition. Both topics are of vital interest to researchers in various mathematical and statistical fields and have never before been treated together in one book. The use of data random graphs in pattern recognition in clustering and classification is discussed, and the applications for both disciplines are enhanced with new tools for the statistical pattern recognition community. New and i 410 0$aWiley series in probability and statistics. 606 $aRandom graphs 606 $aPattern perception$xStatistical methods 606 $aPattern recognition systems 615 0$aRandom graphs. 615 0$aPattern perception$xStatistical methods. 615 0$aPattern recognition systems. 676 $a511.5 676 $a511/.5 700 $aMarchette$b David J$066281 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910830590103321 996 $aRandom graphs for statistical pattern recognition$94028148 997 $aUNINA