02691cam a22003495a 450000100090000000300090000900500170001800800410003501000170007602000430009302000310013604000180016705000220018508200160020710000190022324501370024225000120037926000380039130000410042949000460047050400680051652014820058465000300206665000180209665000440211465000210215865000280217965000330220770000230224070000330226383000450229615512507BR-ReIFE20200626104343.0081106s2009    nyua     b    001 0 eng    a  2008941148  a9780387848570 (hardcover : alk. paper)  a9780387848587 (electronic)  aDLCcDLCdDLC00aQ325.5b.H39 200900a006.3/12221 aHastie, Trevor14aThe elements of statistical learning :bdata mining, inference, and prediction /cTrevor Hastie, Robert Tibshirani, Jerome Friedman.  a2nd ed.  aNew York, NY :bSpringer,cc2009.  axxii, 745 p. :bil. ; col. ;c25 cm.0 aSpringer series in statistics,x0172-7397  aInclui bibliografia, referências e índices. (p. [699]-727).3 aDuring the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas 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 color 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.

This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression and path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for ``wide'' data (p bigger than n), including multiple testing and false discovery rates.04aAprendizado do computador04aEstatística04aMineração de dados (Computação)04aBioinformática04aInferência (Lógica)04aInteligência computacional1 aTibshirani, Robert1 aFriedman, J. H.q(Jerome H.)  aSpringer Series in Statistics (Springer)