The Elements Of Statistical Learning - Data Mining, Inference, And Prediction - Friedman Jerome
- Collection: Springer Series In Statistics
- Format: Relié Voir le descriptif
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Présentation The Elements Of Statistical Learning - Data Mining, Inference, And Prediction de Friedman Jerome Format Relié
- Livre Mathématiques
Résumé :
During the past decade, there has been an explosion in computation and information technology. With it has 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 different terminology. This book describes the important ideas in these areas in common conceptual framework. While the approach is statistical, the emphasis 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 machines, classification trees, and boosting - the first comprehensive treatment of this topic in any book.
Biographie:
Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University and prominent researchers in this area. Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in 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, and projection pursuit.
Sommaire:
["Overview of Supervised Learning","Linear methods for Regression","Linear methods for Classification","Basis Expansions and Regularization","Kernel methods","Model Assessment and Selection","Model Inference AND Averaging","Additive Models, Trees, and Related Methods","Boosting and Additive Trees","Neural Networks","Support Vector Machines and Flexible Discriminants","Prototype Methods and nearest-Neighbors","Unsupervised Learning"]
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