文件名称：The elements of statistical learning-data mining, inference and prediction
文件大小：12.16MB
文件格式：PDF
更新时间：2018-07-15 05:12:25
machine learning
During the past decade there has been an explosion in computation andinformation technology. With it has come vast amounts of data in avariety of fields such as medicine, biology, finance, and marketing.The challenge of understanding these data has led to the development ofnew tools in the field of statistics, and spawned new areas such asdata mining, machine learning, and bioinformatics. Many of these toolshave common underpinnings but are often expressed with differentterminology. This book descibes the important ideas in these areas in acommon conceptual framework. While the approach is statistical, theemphasis is on concepts rather than mathematics. Many examples aregiven, with a liberal use of color graphics. It should be a valuableresource for statisticians and anyone interested in data mining inscience or industry. The book's coverage is broad, from supervisedlearing (prediction) to unsupervised learning. The many topics includeneural networks, support vector machines, classification trees andboosting--the first comprehensive treatment of this topic in any book.Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors ofstatistics at Stanford University. They are prominent researchers inthis area: Hastie and Tibshirani developed generalized additive modelsand wrote a popular book of that title. Hastie wrote much of thestatistical modeling software in S-PLUS and invented principal curvesand surfaces. Tibshirani proposed the Lasso and is co-author of thevery successful An Introduction to the Bootstrap. Friedman is theco-inventor of many data-mining tools including CART, MARS, andprojection pursuit.