Interactively Exploring High-Dimensional Data and Models in R
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Interactively Exploring High-Dimensional Data and Models in R
Laa, Ursula; Cook, Dianne
Taylor & Francis Ltd
04/2026
272
Dura
Inglês
9781032748443
Pré-lançamento - envio 15 a 20 dias após a sua edição
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Preface I Introduction 1 Picturing high dimensions 2 Technical details II Dimension reduction 3 Dimension reduction overview 4 Principal component analysis 5 Non-linear dimension reduction III Cluster analysis 6 Introduction to clustering 7 Spin-and-brush approach 8 Hierarchical clustering 9 k-means clustering 10 Model-based clustering 11 Self-organizing maps 12 Summarising and comparing clustering results IV Supervised classification 13 Introduction to supervised classification 14 Linear discriminant analysis 15 Trees and forests 16 Support vector machines 17 Neural networks and deep learning 18 Diagnostics for classification models References Appendices A Toolbox B Data C Links to Book Code and Additional Data D Glossary Index
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dimension reduction techniques;principal component analysis;hierarchical clustering methods;k-means algorithm;supervised classification models;neural networks explainability;visual diagnostics for statistical models
Preface I Introduction 1 Picturing high dimensions 2 Technical details II Dimension reduction 3 Dimension reduction overview 4 Principal component analysis 5 Non-linear dimension reduction III Cluster analysis 6 Introduction to clustering 7 Spin-and-brush approach 8 Hierarchical clustering 9 k-means clustering 10 Model-based clustering 11 Self-organizing maps 12 Summarising and comparing clustering results IV Supervised classification 13 Introduction to supervised classification 14 Linear discriminant analysis 15 Trees and forests 16 Support vector machines 17 Neural networks and deep learning 18 Diagnostics for classification models References Appendices A Toolbox B Data C Links to Book Code and Additional Data D Glossary Index
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
