All of these eCoupons exclude Tesco Partners. Please enter the eCoupon code at the Tesco direct checkout to qualify. These eCoupons are only valid until 02/07/14. Orders must be placed by the stated end date to qualify. eCoupons can be used online at the Tesco direct checkout by entering the eCoupon code. These eCoupons are valid online only. Each eCoupon code can only be used once per customer. eCoupon codes starting TDX cannot be used with any other eCoupon code. All products are subject to availability and if stocked online. These eCoupons are, and shall remain the property of Tesco Stores Ltd and are not for re-sale or publication. We reserve the right to withdraw any of these eCoupons at any time before the published end date. Delivery costs are excluded from the minimum spend values.
During 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 & 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.
Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in RS-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.