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Jerome Friedman
Kirjat ja teokset yhdessä paikassa: 3 kirjaa, julkaisuja vuosilta 1984-2014, suosituimpien joukossa The Elements of Statistical Learning. Vertaile teosten hintoja ja tarkista saatavuus suomalaisista kirjakaupoista.
An Ordinary Man...A Life Changing Story An incredibly powerful faith-filled story of how Jerome Friedman, an ordinary man, courageously battled three cancers. During this journey, he experienced an extraordinary gift from God...true joy. This gift left him free from all life stresses filling him with a genuine lasting peace. He humbly shares his story with you so that you may embrace everlasting joy for the rest of your life.
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 & 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 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.