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The Elements of Statistical Learning ; Data Mining, Inference, and Prediction

By: Contributor(s): Language: EN, englisch Series: Springer Series in Statistics | Bickel, P. (Ed.) | Diggle, P. (Ed.) | Fienberg, S. (Ed.) | et alPublication details: New York, Berlin, Heidelberg : Springer-Verlag 2001Edition: 1. EdDescription: xvi, 533 pp., 200 Full-Color IllustrationsISBN:
  • 0-387-95284-5
Subject(s): DDC classification:
  • 006 Special computer methods
Online resources:
Contents:
from the Table of Contents: Preface; Introduction; Overview of Supervised Learning; Linear Methods for Regression; Linear Methods for Classification; Basis Expansions and Regularization; Kernel Methods; Model Assessment and Selection; Model Inference and Averaging; Additive Models, Trees, and Related Methods; Boosting and Additive Trees; Neural Networks; Support Vector Machines and Flexible Discriminants; Prototype Methods and Nearest-Neighbors; Unsupervised Learning;
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Item type Current library Shelving location Call number Status Date due Barcode
Loanable Institute for Advanced Studies (IHS) Book 17948-A Available IHS102421407

from the Table of Contents: Preface; Introduction; Overview of Supervised Learning; Linear Methods for Regression; Linear Methods for Classification; Basis Expansions and Regularization; Kernel Methods; Model Assessment and Selection; Model Inference and Averaging; Additive Models, Trees, and Related Methods; Boosting and Additive Trees; Neural Networks; Support Vector Machines and Flexible Discriminants; Prototype Methods and Nearest-Neighbors; Unsupervised Learning;

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