The Elements of Statistical Learning
Stanford University statistics professors Trevor Hastie, Robert Tibshirani, and Jerome Friedman They are well-known researchers in this field: Hastie and Tibshirani created generalized additive models and wrote a popular book with the same name. Hastie invented principal curves and surfaces and co-developed much of the statistical modeling software and environment in R/S-PLUS. Tibshirani invented the lasso and is co-author of the best-selling An Introduction to Bootstrap. Friedman co-invented numerous data-mining tools, including CART, MARS, projection pursuit, and gradient boosting.
In a common conceptual framework, The Elements of Statistical Learning describes important ideas in a variety of fields such as medicine, biology, finance, and marketing. Despite the statistical approach, the emphasis is on concepts rather than mathematics. Many examples are provided, with extensive use of color graphics. It's an excellent resource for statisticians and anyone else interested in data mining in science or industry. The book covers a wide range of topics, from supervised learning (prediction) to unsupervised learning. Among the numerous topics covered are neural networks, support vector machines, classification trees, and boosting, which is the first comprehensive treatment of this topic in any book.
Many topics not covered in the original are covered in this major new edition, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorisation, and spectral clustering. A chapter on methods for "wide" data (p greater than n), including multiple testing and false discovery rates, is also included.
Author: Trevor Hastie, Robert Tibshirani and Jerome Friedman
Link to buy: https://www.amazon.com/gp/product/0387848576/
Ratings: 4.6 out of 5 stars (from 4025 reviews)
Best Sellers Rank: #19,781 in Books
#8 in Data Mining (Books)
#13 in Artificial Intelligence & Semantics
#24 in Probability & Statistics (Books)
