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## Details

Genre/Form: | Electronic books |
---|---|

Additional Physical Format: | Print version: Hastie, Trevor. Elements of statistical learning. New York : Springer, ©2001 (DLC) 2001031433 |

Material Type: | Document, Internet resource |

Document Type: | Internet Resource, Computer File |

All Authors / Contributors: |
Trevor Hastie; Robert Tibshirani; J H Friedman |

ISBN: | 9780387216065 0387216065 6610187436 9786610187430 |

OCLC Number: | 133158147 |

Description: | 1 online resource |

Contents: | Overview of Supervised Learning -- Linear Methods for Regression -- Linear Methods for Classification -- Basic 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 Discriminates -- Prototype Methods and Nearest Neighbors -- Unsupervised Learning. |

Series Title: | Springer series in statistics. |

Responsibility: | Trevor Hastie, Robert Tibshirani, Jerome Friedman. |

### Abstract:

Contains topics that include neural networks, support vector machines, classification trees and boosting. This book also covers graphical models, random forests, ensemble methods, least angle regression and path algorithms for the lasso, non-negative matrix factorization, and spectral clustering.
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