The elements of statistical learning : data mining, inference, and prediction / Trevor Hastie
By: Hastie, Trevor.
Contributor(s): Robert Tibshirani | Jerome Friedman.
Material type:
BookSeries: Springer series in statistics.Publisher: New York : Springer, 2001Description: xxii, 745 p. ill.Subject(s): Supervised learning (Machine learning) | Inteligencia artificial | Apprentissage supervisé (Intelligence artificielle)DDC classification: 519.5 H356E 2001 Summary: Describes important statistical ideas in machine learning, data mining, and bioinformatics. Covers a broad range, from supervised learning (prediction), to unsupervised learning, including classification trees, neural networks, and support vector machines.
| Item type | Current location | Collection | Call number | Status | Date due | Barcode | Item holds |
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Books
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Information Technology University, Lahore General Stacks | Non-fiction | 519.5 H356E 2001 c.2 (Browse shelf) | Available | 003824 | ||
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Books
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Information Technology University, Lahore General Stacks | Non-fiction | 519.5 H356E 2001 (Browse shelf) | Checked out to Mr. Ali Ahmed (1075) | 28/02/2022 | 003109 |
Total holds: 0
includes index.
Describes important statistical ideas in machine learning, data mining, and bioinformatics. Covers a broad range, from supervised learning (prediction), to unsupervised learning, including classification trees, neural networks, and support vector machines.
Hbk.
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