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The elements of statistical learning / Trevor Hastie

No review available on this record. Title : The elements of statistical learning : data mining, inference, and prediction Material Type: printed text Authors: Trevor Hastie, Author ; Robert Tibshirani, Author ; Jerome Friedman, Author Edition statement: 2nd edition Publisher: New York : Springer Publication Date: 2013 Series: Springer texts in statistics Pagination: xxii; 745 p. Layout: ill. Size: 24 cm ISBN (or other code): 978-0-387-84857-0 Price: 69,95 € General note: Includes bibliographical references (p. [699]-727) Includes index (p. [729]-745) Languages : English (eng) Original Language : English (eng) Descriptors: Bioinformatics Computational intelligence Data mining Mathematical statistics Research - Statistical methods Class number: 006.3 Abstract: 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. Contents note: Overview of Supervised Learning; Linear Methods for Regression; Linear Methods for Classification; Basis Expansions and Regularization; Kernel Smoothing 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; Random Forests; Ensemble Learning; Undirected Graphical Models; High-Dimensional Problems: p N; Record link: https://library.seeu.edu.mk/index.php?lvl=notice_display&id=16107

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Barcode	Call number	Media type	Location	Section	Status
1702-001591	006.3 Has-ele 2013	General Collection	Library "Max van der Stoel"	English	Available

An introduction to statistical learning / James Gareth

No review available on this record. Title : An introduction to statistical learning : with applications in R Material Type: printed text Authors: James Gareth, Author ; Daniela Witten, Author ; Trevor Hastie, Author ; Robert Tibshirani, Author Publisher: New York : Springer Publication Date: 2013 Series: Springer texts in statistics Pagination: xiv; 426 p. Layout: ill. Size: 26 cm ISBN (or other code): 978-1-461-47137-0 Price: 59,99 € General note: Includes bibliographical references Includes index (p. 419-426) Languages : English (eng) Original Language : English (eng) Descriptors: Mathematical models Mathematical statistics R (Computer program language) Class number: 519.5 Abstract: An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential tool set for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cuttingedge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra. Contents note: Statistical Learning; Linear Regression; Classification; Resampling Methods; Linear Model Selection and Regularization; Moving Beyond Linearity; Tree-Based Methods; Support Vector Machines; Unsupervised Learning; Record link: https://library.seeu.edu.mk/index.php?lvl=notice_display&id=16104

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Barcode	Call number	Media type	Location	Section	Status
1702-001589	519.5 Gar-int 2013	General Collection	Library "Max van der Stoel"	English	Available