Hiển thị biểu ghi dạng vắn tắt

dc.contributor.authorHastie, Trevor
dc.contributor.authorTibshirani, Robert
dc.contributor.authorFriedman, Jerome
dc.date.issued2017
dc.identifier.isbn978-0-387-84858-7
dc.identifier.urihttps://thuvienso.hoasen.edu.vn/handle/123456789/10524
dc.descriptionxxii, 745 p. : ill.
dc.description.abstractThis 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 & 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.
dc.language.isoen
dc.publisherSpringer
dc.subjectStatistics
dc.subject.otherMachine learning
dc.subject.otherData mining
dc.subject.otherBioinformatics
dc.subject.otherInference
dc.subject.otherForecasting
dc.subject.otherComputational intelligence
dc.titleThe elements of statistical learning : data mining, inference, and prediction
dc.typeBook
dc.description.version2nd edition


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Hiển thị biểu ghi dạng vắn tắt