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Make a suggestion Refine your searchThe elements of statistical learning / Trevor Hastie
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 methodsClass 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 Hold
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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
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 Hold
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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
Title : Making sense of data : a practical guide to exploratory data analysis and data mining Material Type: printed text Authors: Glenn J. Myatt, Author Publisher: New York : Wiley-Interscience Publication Date: 2007 Pagination: xii, 280 p. Layout: ill. Size: 24 cm. ISBN (or other code): 978-0-470-07471-8 General note: Includes bibliographical references (p. 273-274)
Includes index (p. 275-280)
Languages : English (eng) Original Language : English (eng) Descriptors: Data mining
Mathematical statisticsClass number: 006.3 Abstract: Disciplines as diverse as biology, economics, engineering, and marketing measure, gather and store data primarily in electronic databases. For example, retail companies store information on sales transactions, insurance companies keep track of insurance claims, and meteorological organizations measure and collect data concerning weather conditions. Timely and well-founded decisions need to be made using the information collected. These decisions will be used to maximize sales, improve research and development projects and trim costs. Retail companies must be able to understand what products in which stores are performing well, insurance companies need to identify activities that lead to fraudulent claims, and meteorological organizations attempt to predict future weather conditions. The process of taking the raw data and converting it into meaningful information necessary to make decisions is the focus of this book. It is practically impossible to make sense out of data sets containing more than a handful of data points without the help of computer programs. Many free and commercial software programs exist to sift through data, such as spreadsheets, data visualization software, statistical packages, OLAP (On-Line Analytical Processing) applications, and data mining tools. Deciding what software to use is just one of the questions that must be answered. In fact, there are many issues that should be thought through in any exploratory data analysis/data mining project. Following a predefined process will ensure that issues are addressed and appropriate steps are taken. Contents note: Introduction; Definition; Preparation; Tables and graphs; Statistics; Grouping; Prediction; Deployment; Conclusions; Record link: https://library.seeu.edu.mk/index.php?lvl=notice_display&id=14316 Hold
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Barcode Call number Media type Location Section Status 1702-000841 006.3 Mya-Mak 2007 General Collection Library "Max van der Stoel" English Due for return by 08/16/2022
Title : New Cambridge Statistical Tables Material Type: printed text Authors: D.V. Lindley, Author ; W.F. Scott, Author Edition statement: 2nd edition Publisher: Cambridge : Cambridge University Press Publication Date: 2008 Pagination: 95 p. Size: 29 cm ISBN (or other code): 978-0-521-48485-5 Price: 8.89 euro Languages : English (eng) Original Language : English (eng) Descriptors: Mathematical statistics
Statistical tablesClass number: 519.5 Abstract: The latest edition of this very successful and authoritative set of tables still benefits from clear typesetting, which makes the figures easy to read and use. It has, however, been improved by the addition of new tables that provide Bayesian confidence limits for the binomial and Poisson distributions, and for the square of the multiple correlation coefficient, which have not been previously available. The intervals are the shortest possible, consistent with the requirement on probability. Great care has been taken to ensure that it is clear just what is being tabulated and how the values may be used; the tables are generally capable of easy interpolation. The book contains all the tables likely to be required for elementary statistical methods in the social, business and natural sciences. It will be an essential aid for teachers, researchers and students in those subjects where statistical analysis is not wholly carried out by computers... Contents note: The binomial distribution function; The Poisson distribution function; Binomial coefficients; The normal distribution function; Percentage points of the normal distribution; Logarithms of factorials; The chi-squared distribution function; Percentage points of the chi-squared distribution; The t-distribution function; Percentage points of the t-distribution; Percentage points of Behrens' distribution; Percentage points of the F-distribution; Percentage points of the correlation coefficient r when rho = 0; Percentage points of Spearman's S; Percentage points of Kendall's K; The z-transformation of the correlation coefficient; The inverse of the z-transformation; Percentage points of the distribution of the number of runs; Upper percentage points of the two-sample Kolmogorov-Smirnov distribution; Percentage points of Wilcoxon's signed-rank distribution; Percentage points of the Mann-Whitney distribution; Expected values of normal order statistics (normal scores); Sums of squares of normal scores; Upper percentage points of the one-sample Kolmogorov-Smirnov distribution; Upper percentage points of Friedmann's distribution; Upper percentage points of the Kruskal-Wallis distribution; Hypergeometric probabilities; Random sampling numbers; Random normal deviates; Bayesian confidence limits for a binomial parameter; Bayesian confidence limits for a Poisson mean; Bayesian confidence limits for the square of a multiple correlation coefficient; Record link: https://library.seeu.edu.mk/index.php?lvl=notice_display&id=14309 Hold
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Barcode Call number Media type Location Section Status 1702-000848 519.5 Lin-New 2008 General Collection Library "Max van der Stoel" English Available
Title : The numerati Material Type: printed text Authors: Stephen Baker, Author Publisher: Mariner Books Houghton Mifflin Harcourt Publication Date: 2008 Pagination: 244 p. Size: 21 cm ISBN (or other code): 978-0-547-24793-9 General note: Includes bibliographical reference and index Languages : English (eng) Original Language : English (eng) Descriptors: Data processing
Human behavior
Mathematical models
Mathematical statisticsClass number: 303.48 Abstract: "The Numerati"-Every day we produce loads of data about ourselves simply by living in the modern world: we click web pages, flip channels, drive through automatic toll booths, shop with credit cards, and make cell phone calls. Now, in one of the greatest undertakings of the twenty-first century, a savvy group of mathematicians and computer scientists is beginning to sift through this data to dissect us and map out our next steps. Their goal? To manipulate our behavior -- what we buy, how we vote -- without our even realizing it. Contents note: Worker; Shopper; Voter; Blogger; Terrorist; Patient; Lover; Conclusion; Record link: https://library.seeu.edu.mk/index.php?lvl=notice_display&id=14490 Hold
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Barcode Call number Media type Location Section Status 1702-001377 303.48 Bak-num 2008 General Collection Library "Max van der Stoel" English Available
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