The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)

Robert Tibshirani


During the previous decade there was an explosion in computation and data expertise. With it have come significant quantities of information in various fields reminiscent of medication, biology, finance, and advertising. The problem of figuring out those information has resulted in the advance of recent instruments within the box of statistics, and spawned new parts equivalent to info mining, computing device studying, and bioinformatics. lots of those instruments have universal underpinnings yet are usually expressed with diversified terminology. This booklet describes the real rules in those components in a typical conceptual framework. whereas the strategy is statistical, the emphasis is on options instead of arithmetic. Many examples are given, with a liberal use of colour photographs. It is a helpful source for statisticians and an individual drawn to information mining in technology or undefined. The book's assurance is wide, from supervised studying (prediction) to unsupervised studying. the numerous subject matters contain neural networks, help vector machines, category bushes and boosting---the first accomplished therapy of this subject in any book.

This significant new version good points many subject matters no longer lined within the unique, together with graphical versions, random forests, ensemble tools, least perspective regression & direction algorithms for the lasso, non-negative matrix factorization, and spectral clustering. there's additionally a bankruptcy on equipment for ``wide'' information (p larger than n), together with a number of checking out and fake discovery rates.

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