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- An Introduction to Statistical Learning

- Author : Gareth James
- Publsiher : Springer Science & Business Media
- Release : 24 June 2013
- ISBN : 1461471389
- Pages : 426 pages
- Rating : 5/5 from 2 reviews

GET THIS BOOKAn Introduction to Statistical Learning

Download or read book entitled *An Introduction to Statistical Learning* by author: *Gareth James* which was release on *24 June 2013* and published by *Springer Science & Business Media* with total page 426 pages . This book available in PDF, EPUB and Kindle Format. An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset 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 cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

- Author : Gareth James,Daniela Witten,Trevor Hastie,Robert Tibshirani
- Publisher : Springer Science & Business Media
- Release Date : 2013-06-24
- Total pages : 426
- ISBN : 1461471389

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**Summary :** An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset 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 ...

- Author : Sanjeev Kulkarni,Gilbert Harman
- Publisher : John Wiley & Sons
- Release Date : 2011-06-09
- Total pages : 288
- ISBN : 1461471389

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**Summary :** A thought-provoking look at statistical learning theory and its role in understanding human learning and inductive reasoning A joint endeavor from leading researchers in the fields of philosophy and electrical engineering, An Elementary Introduction to Statistical Learning Theory is a comprehensive and accessible primer on the rapidly evolving fields of ...

- Author : Daniel D. Gutierrez
- Publisher : Technics Publications
- Release Date : 2015-11-01
- Total pages : 282
- ISBN : 1461471389

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**Summary :** A practitionerâ€™s tools have a direct impact on the success of his or her work. This book will provide the data scientist with the tools and techniques required to excel with statistical learning methods in the areas of data access, data munging, exploratory data analysis, supervised machine learning, unsupervised ...

- Author : Peter Forrest
- Publisher : Createspace Independent Publishing Platform
- Release Date : 2017-07-04
- Total pages : 422
- ISBN : 1461471389

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**Summary :** 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 ...

- Author : Lise Getoor,Ben Taskar
- Publisher : MIT Press
- Release Date : 2007
- Total pages : 586
- ISBN : 1461471389

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**Summary :** Advanced statistical modeling and knowledge representation techniques for a newly emerging area of machine learning and probabilistic reasoning; includes introductory material, tutorials for different proposed approaches, and applications. Handling inherent uncertainty and exploiting compositional structure are fundamental to understanding and designing large-scale systems. Statistical relational learning builds on ideas from ...

- Author : Trevor Hastie,Robert Tibshirani,Jerome Friedman
- Publisher : Springer Science & Business Media
- Release Date : 2013-11-11
- Total pages : 536
- ISBN : 1461471389

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**Summary :** 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 ...

- Author : Vladimir Vapnik
- Publisher : Springer Science & Business Media
- Release Date : 1999-11-19
- Total pages : 314
- ISBN : 1461471389

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**Summary :** The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning ...

- Author : Arthur Glenberg,Matthew Andrzejewski
- Publisher : Routledge
- Release Date : 2007-08-09
- Total pages : 580
- ISBN : 1461471389

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**Summary :** Learning from Data reviews the basics of statistical reasoning to help students understand psychological data that affect their lives. To facilitate learning the authors devote extra attention to explaining the difficult concepts, use repetition to enhance memory and illustrate concepts with numerous examples. A six-step procedure helps students apply all ...

- Author : Masashi Sugiyama
- Publisher : Morgan Kaufmann
- Release Date : 2015-10-31
- Total pages : 534
- ISBN : 1461471389

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**Summary :** Machine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and machine learning are combined together they are a powerful tool for analysing various kinds of data in many computer science/engineering areas including, image processing, speech processing, natural language processing, robot control, as ...

- Author : Kieth A. Carlson,Jennifer R. Winquist
- Publisher : SAGE Publications
- Release Date : 2013-01-09
- Total pages : 576
- ISBN : 1461471389

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**Summary :** An Introduction to Statistics is the ideal text for incorporating an active learning approach to the subject of introductory statistics. Authors Kieth A. Carlson and Jennifer R. Winquist carefully explain fundamental statistical concepts in short, easy-to-understand chapters, then use empirically developed workbook activities to both reinforce and expand on these ...

- Author : Vladimir N. Vapnik,VLADIMIR AUTOR VAPNIK
- Publisher : Wiley-Interscience
- Release Date : 1998-09-30
- Total pages : 736
- ISBN : 1461471389

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**Summary :** Introduction: The Problem of Induction and Statistical Inference. Two Approaches to the Learning Problem. Appendix to Chapter1: Methods for Solving III-Posed Problems. Estimation of the Probability Measure and Problem of Learning. Conditions for Consistency of Empirical Risk Minimization Principle. Bounds on the Risk for Indicator Loss Functions. Appendix to Chapter 4: ...

- Author : R. Lyman Ott,Micheal T. Longnecker
- Publisher : Cengage Learning
- Release Date : 2008-12-30
- Total pages : 1296
- ISBN : 1461471389

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**Summary :** Ott and Longnecker's AN INTRODUCTION TO STATISTICAL METHODS AND DATA ANALYSIS, Sixth Edition, provides a broad overview of statistical methods for advanced undergraduate and graduate students from a variety of disciplines who have little or no prior course work in statistics. The authors teach students to solve problems encountered in ...

- Author : Trevor Hastie,Robert Tibshirani,Martin Wainwright
- Publisher : CRC Press
- Release Date : 2015-05-07
- Total pages : 367
- ISBN : 1461471389

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**Summary :** Discover New Methods for Dealing with High-Dimensional Data A sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help ...

- Author : Ke-Lin Du,M. N. S. Swamy
- Publisher : Springer Nature
- Release Date : 2019-09-12
- Total pages : 988
- ISBN : 1461471389

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**Summary :** This book provides a broad yet detailed introduction to neural networks and machine learning in a statistical framework. A single, comprehensive resource for study and further research, it explores the major popular neural network models and statistical learning approaches with examples and exercises and allows readers to gain a practical ...

- Author : Joe Suzuki
- Publisher : Springer Nature
- Release Date : 2021-04-17
- Total pages : 212
- ISBN : 1461471389

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**Summary :** Read online Statistical Learning with Math and R written by Joe Suzuki, published by Springer Nature which was released on . Download full Statistical Learning with Math and R Books now! Available in PDF, ePub and Kindle....