Machine Learning in Medicine

Machine learning is a novel discipline concerned with the analysis of large and multiple variables data. It involves computationally intensive methods, like factor analysis, cluster analysis, and discriminant analysis. It is currently mainly the domain of computer scientists, and is already commonly...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Cleophas, Ton J. (Autor), Zwinderman, Aeilko H. (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Dordrecht : Springer Netherlands : Imprint: Springer, 2013.
Edición:1st ed. 2013.
Temas:
Medicine-Research.
Biology-Research.
Invertebrates.
Medical sciences.
Statistics .
Image processing-Digital techniques.
Computer vision.
Literacy.
Biomedical Research.
Invertebrate Zoology.
Health Sciences.
Statistics.
Computer Imaging, Vision, Pattern Recognition and Graphics.
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • Preface
  • 1 Introduction to machine learning
  • 2 Logistic regression for health profiling
  • 3 Optimal scaling: discretization
  • 4 Optimal scaling: regularization including ridge, lasso, and elastic net regression
  • 5 Partial correlations
  • 6 Mixed linear modelling
  • 7 Binary partitioning
  • 8 Item response modelling
  • 9 Time-dependent predictor modelling
  • 10 Seasonality assessments
  • 11 Non-linear modelling
  • 12 Artificial intelligence, multilayer Perceptron modelling
  • 13 Artificial intelligence, radial basis function modelling
  • 14 Factor analysis
  • 15 Hierarchical cluster analysis for unsupervised data
  • 16 Partial least squares
  • 17 Discriminant analysis for Supervised data
  • 18 Canonical regression
  • 19 Fuzzy modelling
  • 20 Conclusions. Index.                                                                                                                                                                                                                                                                                                                                                .

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