000			01218nam a22002057a 4500
003			OSt
005			20251129104952.0
008			251129b |||||||| |||| 00| 0 eng d
020			_a9780387848570
040			_cSoET Library
082			_a006.31 HAS
100			_aHastie,Trevor _92891
245			_aElements of Statistical Learning: Data Mining, Inference, and Prediction
250			_a2
260			_aNew York: _bSpringer, _c2009.
300			_axxii, ill., 745 p.
440			_aSpringer Series in Statistics _92892
520			_aThis book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book.
942			_2ddc _cBK
999			_c10095 _d10095