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The 2x2 matrix : contingency, confusion and the metrics of binary classification / / A. J. Larner
The 2x2 matrix : contingency, confusion and the metrics of binary classification / / A. J. Larner
Autore Larner A. J.
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (175 pages)
Disciplina 519.56
Soggetto topico Contingency tables
Informàtica mèdica
Soggetto genere / forma Llibres electrònics
ISBN 9783030749200
9783030749194
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910520073503321
Larner A. J.  
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
The 2x2 matrix : contingency, confusion and the metrics of binary classification / / A. J. Larner
The 2x2 matrix : contingency, confusion and the metrics of binary classification / / A. J. Larner
Autore Larner A. J.
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (175 pages)
Disciplina 519.56
Soggetto topico Contingency tables
Informàtica mèdica
Soggetto genere / forma Llibres electrònics
ISBN 9783030749200
9783030749194
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISA-996466554203316
Larner A. J.  
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Linear statistical analysis of discrete data / Mikel Aickin
Linear statistical analysis of discrete data / Mikel Aickin
Autore Aickin, Mikel
Descrizione fisica xvi, 358 p. : ill. ; 24 cm.
Disciplina 519.56
Collana Wiley series in probability and mathematical statistics. Applied Probability and Statistics, 0271-6356
Soggetto topico Contingency tables
ISBN 0471097748
Classificazione AMS 62H17
QA277
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISALENTO-991001089439707536
Aickin, Mikel  
Materiale a stampa
Lo trovi qui: Univ. del Salento
Opac: Controlla la disponibilità qui
Spectral clustering and biclustering [[electronic resource] ] : learning large graphs and contingency tables / / Marianna Bolla
Spectral clustering and biclustering [[electronic resource] ] : learning large graphs and contingency tables / / Marianna Bolla
Autore Bolla Marianna
Pubbl/distr/stampa Chichester, West Sussex, United Kingdom, : John Wiley & Sons Inc., 2013
Descrizione fisica 1 online resource (294 p.)
Disciplina 515/.35
Soggetto topico Contingency tables
Graph theory
Multivariate analysis
ISBN 1-118-65071-9
1-118-65068-9
1-118-65070-0
Classificazione MAT029000
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Machine generated contents note: Dedication Preface Acknowledgements List of Abbreviations Introduction 1 Multivariate analysis techniques for representing graphs and contingency tables 1.1 Quadratic placement problems for weighted graphs and hypergraphs 1.1.1 Representation of edge-weighted graphs 1.1.2 Representation of hypergraphs 1.1.3 Examples for spectra and representation of simple graphs 1.2 SVD of contingency tables and correspondence matrices 1.3 Normalized Laplacian and modularity spectra 1.4 Representation of joint distributions 1.4.1 General setup 1.4.2 Integral operators between L2 spaces 1.4.3 When the kernel is the joint distribution itself 1.4.4 Maximal correlation and optimal representations 1.5 Treating nonlinearities via reproducing kernel Hilbert spaces 1.5.1 Notion of the reproducing kernel 1.5.2 RKHS corresponding to a kernel 1.5.3 Two examples of an RKHS 1.5.4 Kernel - based on a sample - and the empirical feature map References 2 Multiway cut problems 2.1 Estimating multiway cuts via spectral relaxation 2.1.1 Maximum, minimum, and ratio cuts of edge-weighted graphs 2.1.2 Multiway cuts of hypergraphs 2.2 Normalized cuts 2.3 The isoperimetric number and sparse cuts 2.4 The Newman-Girvan modularity 2.4.1 Maximizing the balanced Newman-Girvan modularity 2.4.2 Maximizing the normalized Newman-Girvan modularity 2.4.3 Anti-community structure and some examples 2.5 Normalized bicuts of contingency tables References 3 Large networks, perturbation of block structures 3.1 Symmetric block structures burdened with random noise 3.1.1 General blown-up structures 3.1.2 Blown-up multipartite structures 3.1.3 Weak links between disjoint components 3.1.4 Recognizing the structure 3.1.5 Random power law graphs and the extended planted partition model 3.2 Noisy contingency tables 3.2.1 Singular values of a noisy contingency table 3.2.2 Clustering the rows and columns via singular vector pairs 3.2.3 Perturbation results for correspondence matrices 3.2.4 Finding the blown-up skeleton 3.3 Regular cluster pairs 3.3.1 Normalized modularity and volume regularity of edgeweighted graphs 3.3.2 Correspondence matrices and volume regularity of contingency tables 3.3.3 Directed graphs References 4 Testable graph and contingency table parameters 4.1 Convergent graph sequences 4.2 Testability of weighted graph parameters 4.3 Testability of minimum balanced multiway cuts 4.4 Balanced cuts and fuzzy clustering 4.5 Noisy graph sequences 4.6 Convergence of the spectra and spectral subspaces 4.7 Convergence of contingency tables References 5 Statistical learning of networks 5.1 Parameter estimation in random graph models 5.1.1 EMalgorithmfor estimating the parameters of the block model 5.1.2 Parameter estimation in the _ and _ models 5.2 Nonparametric methods for clustering networks 5.2.1 Spectral clustering of graphs and biclustering of contingency tables 5.2.2 Clustering of hypergraphs 5.3 Supervised learning References A Linear algebra and some functional analysis A.1 Metric, normed vector, and Euclidean spaces A.2 Hilbert spaces A.3 Matrices References B Random vectors and matrices B.1 Random vectors B.2 Random matrices References C Multivariate statistical methods C.1 Principal Component Analysis C.2 Canonical Correlation Analysis C.3 Correspondence Analysis C.4 Multivariate Regression and Analysis of Variance C.5 The k-means clustering C.6 Multidimensional Scaling C.7 Discriminant Analysis References Index .
Record Nr. UNINA-9910141845203321
Bolla Marianna  
Chichester, West Sussex, United Kingdom, : John Wiley & Sons Inc., 2013
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Spectral clustering and biclustering : learning large graphs and contingency tables / / Marianna Bolla
Spectral clustering and biclustering : learning large graphs and contingency tables / / Marianna Bolla
Autore Bolla Marianna
Edizione [1st ed.]
Pubbl/distr/stampa Chichester, West Sussex, United Kingdom, : John Wiley & Sons Inc., 2013
Descrizione fisica 1 online resource (294 p.)
Disciplina 515/.35
Soggetto topico Contingency tables
Graph theory
Multivariate analysis
ISBN 1-118-65071-9
1-118-65068-9
1-118-65070-0
Classificazione MAT029000
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Machine generated contents note: Dedication Preface Acknowledgements List of Abbreviations Introduction 1 Multivariate analysis techniques for representing graphs and contingency tables 1.1 Quadratic placement problems for weighted graphs and hypergraphs 1.1.1 Representation of edge-weighted graphs 1.1.2 Representation of hypergraphs 1.1.3 Examples for spectra and representation of simple graphs 1.2 SVD of contingency tables and correspondence matrices 1.3 Normalized Laplacian and modularity spectra 1.4 Representation of joint distributions 1.4.1 General setup 1.4.2 Integral operators between L2 spaces 1.4.3 When the kernel is the joint distribution itself 1.4.4 Maximal correlation and optimal representations 1.5 Treating nonlinearities via reproducing kernel Hilbert spaces 1.5.1 Notion of the reproducing kernel 1.5.2 RKHS corresponding to a kernel 1.5.3 Two examples of an RKHS 1.5.4 Kernel - based on a sample - and the empirical feature map References 2 Multiway cut problems 2.1 Estimating multiway cuts via spectral relaxation 2.1.1 Maximum, minimum, and ratio cuts of edge-weighted graphs 2.1.2 Multiway cuts of hypergraphs 2.2 Normalized cuts 2.3 The isoperimetric number and sparse cuts 2.4 The Newman-Girvan modularity 2.4.1 Maximizing the balanced Newman-Girvan modularity 2.4.2 Maximizing the normalized Newman-Girvan modularity 2.4.3 Anti-community structure and some examples 2.5 Normalized bicuts of contingency tables References 3 Large networks, perturbation of block structures 3.1 Symmetric block structures burdened with random noise 3.1.1 General blown-up structures 3.1.2 Blown-up multipartite structures 3.1.3 Weak links between disjoint components 3.1.4 Recognizing the structure 3.1.5 Random power law graphs and the extended planted partition model 3.2 Noisy contingency tables 3.2.1 Singular values of a noisy contingency table 3.2.2 Clustering the rows and columns via singular vector pairs 3.2.3 Perturbation results for correspondence matrices 3.2.4 Finding the blown-up skeleton 3.3 Regular cluster pairs 3.3.1 Normalized modularity and volume regularity of edgeweighted graphs 3.3.2 Correspondence matrices and volume regularity of contingency tables 3.3.3 Directed graphs References 4 Testable graph and contingency table parameters 4.1 Convergent graph sequences 4.2 Testability of weighted graph parameters 4.3 Testability of minimum balanced multiway cuts 4.4 Balanced cuts and fuzzy clustering 4.5 Noisy graph sequences 4.6 Convergence of the spectra and spectral subspaces 4.7 Convergence of contingency tables References 5 Statistical learning of networks 5.1 Parameter estimation in random graph models 5.1.1 EMalgorithmfor estimating the parameters of the block model 5.1.2 Parameter estimation in the _ and _ models 5.2 Nonparametric methods for clustering networks 5.2.1 Spectral clustering of graphs and biclustering of contingency tables 5.2.2 Clustering of hypergraphs 5.3 Supervised learning References A Linear algebra and some functional analysis A.1 Metric, normed vector, and Euclidean spaces A.2 Hilbert spaces A.3 Matrices References B Random vectors and matrices B.1 Random vectors B.2 Random matrices References C Multivariate statistical methods C.1 Principal Component Analysis C.2 Canonical Correlation Analysis C.3 Correspondence Analysis C.4 Multivariate Regression and Analysis of Variance C.5 The k-means clustering C.6 Multidimensional Scaling C.7 Discriminant Analysis References Index .
Record Nr. UNINA-9910812664203321
Bolla Marianna  
Chichester, West Sussex, United Kingdom, : John Wiley & Sons Inc., 2013
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui