This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing 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 colour 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 &amp; path algorithms for the lasso, non-negative matrix factorisation, and spectral clustering. There is also a chapter on methods for "wide'' data (p bigger than n), including multiple testing and false discovery rates.

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

Computers & Technology

Hastie, Trevor

Tibshirani, Robert

Friedman, Jerome

Computer Science

Springer

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 should be 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 &amp; 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 R/S-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.

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

The methodology used to construct tree structured rules is the focus of this monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. Both the practical and theoretical sides have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.

Classification and Regression Trees (Wadsworth Statistics/Probability)

Science & Math

Breiman, Leo

Stone, Charles J.

Olshen, R.A.

Mathematics

Chapman and Hall/CRC

The Most Ancient Testimony: Sixteenth-Century Christian-Hebraica in the Age of Renaissance Nostalgia

The Most Ancient Testimony: Sixteenth-Century Christian-Hebraica in the Age of Renaissance Nostalgia

Swallow Pr

Joy of Life Paperback

Biographies & Memoirs

lulu.com

How did ordinary people, caught up in the violent political and social dislocation of the English Revolution, perceive it? This book attempts an answer through a close study of some 500 newsbooks and pulp publications produced from 1640 to 1660. Like "The Great Cat Massacre" and "The Cheese and the Worms", this work enters the world of enchanted belief, superstition, folk religion and magic. Friedman investigates why Englishmen outside Parliamentary circles were only incidentally concerned with the political, economic and religious questions that have so preoccupied scholars of the period, and why instead, the bestselling issues concerned witches, prodigies, apparitions, divine curses, the re-admittance of Jews to England and an obsession with converting the Turks to Christianity. For the great majority of people the period seems to have caused an upswelling of credulous superstition and bizarre prophecy, rather than the radicalization often assumed. A picture emerges of a frightened, confused society. Friedman examines, too, the Puritan "battle for morality", the attempts to combat ale-drinking, prostitution, pornography and the new fascination with marijuana, and how these issues became entangled in popular assessments of the revolution. Finally, by relating all these concerns to the popular astrological and prophetic literature of the day, the author provides convincing answers to why so many Englishmen ultimately greeted the return of Charles II with joy and relief.

Miracles and the pulp press during the English Revolution: The battle of the frogs and Fairford's flies

UCL Press

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Books by Friedman, Jerome