An Introduction to Statistical Learning

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  • Author : Gareth James
  • Publisher : Springer Science & Business Media
  • Pages : 426 pages
  • ISBN : 1461471389
  • Rating : 5/5 from 2 reviews
CLICK HERE TO GET THIS BOOK >>>An Introduction to Statistical Learning

Download or Read online An Introduction to Statistical Learning full in PDF, ePub and kindle. this book written by Gareth James and published by Springer Science & Business Media which was released on 24 June 2013 with total page 426 pages. We cannot guarantee that An Introduction to Statistical Learning book is available in the library, click Get Book button and read full online book in your kindle, tablet, IPAD, PC or mobile whenever and wherever You Like. 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.

An Introduction to Statistical Learning

An Introduction to Statistical Learning
  • Author : Gareth James,Daniela Witten,Trevor Hastie,Robert Tibshirani
  • Publisher : Springer Science & Business Media
  • Release : 24 June 2013
GET THIS BOOK An Introduction to Statistical Learning

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.

An Elementary Introduction to Statistical Learning Theory

An Elementary Introduction to Statistical Learning Theory
  • Author : Sanjeev Kulkarni,Gilbert Harman
  • Publisher : John Wiley & Sons
  • Release : 09 June 2011
GET THIS BOOK An Elementary Introduction to Statistical Learning Theory

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 statistical pattern recognition and statistical learning theory. Explaining these areas at a level and in a way that is not often found in other books on the topic, the authors

Machine Learning and Data Science

Machine Learning and Data Science
  • Author : Daniel D. Gutierrez
  • Publisher : Technics Publications
  • Release : 01 November 2015
GET THIS BOOK Machine Learning and Data Science

A practitioner’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 machine learning and model evaluation. Machine learning and data science are large disciplines, requiring years of study in order to gain proficiency. This book can be viewed as a set

An Introduction to Statistical Learning

An Introduction to Statistical Learning
  • Author : Peter Forrest
  • Publisher : Createspace Independent Publishing Platform
  • Release : 04 July 2017
GET THIS BOOK An Introduction to Statistical Learning

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

Introduction to Statistical Relational Learning

Introduction to Statistical Relational Learning
  • Author : Lise Getoor,Ben Taskar
  • Publisher : MIT Press
  • Release : 11 May 2021
GET THIS BOOK Introduction to Statistical Relational Learning

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 probability theory and statistics to address uncertainty while incorporating tools from logic, databases and programming languages to represent structure. In Introduction to Statistical Relational Learning, leading researchers in this emerging

The Elements of Statistical Learning

The Elements of Statistical Learning
  • Author : Trevor Hastie,Robert Tibshirani,Jerome Friedman
  • Publisher : Springer Science & Business Media
  • Release : 11 November 2013
GET THIS BOOK The Elements of Statistical Learning

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

Learning From Data

Learning From Data
  • Author : Arthur Glenberg,Matthew Andrzejewski
  • Publisher : Routledge
  • Release : 09 August 2007
GET THIS BOOK Learning From Data

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 statistical tests, from simple to complex. The authors emphasize how to choose the best statistical procedure in the text, the examples and the problems. Intended for undergraduate or graduate statistics

The Nature of Statistical Learning Theory

The Nature of Statistical Learning Theory
  • Author : Vladimir Vapnik
  • Publisher : Springer Science & Business Media
  • Release : 19 November 1999
GET THIS BOOK The Nature of Statistical Learning Theory

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 theory and their connections to fundamental problems in statistics. This second edition contains three new chapters devoted to further development of the learning theory and SVM techniques. Written in a

Introduction to Statistical Machine Learning

Introduction to Statistical Machine Learning
  • Author : Masashi Sugiyama
  • Publisher : Morgan Kaufmann
  • Release : 31 October 2015
GET THIS BOOK Introduction to Statistical Machine Learning

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 well as in fundamental sciences such as biology, medicine, astronomy, physics, and materials. Introduction to Statistical Machine Learning provides a general introduction to machine learning that covers a wide range

An Introduction to Statistics

An Introduction to Statistics
  • Author : Kieth A. Carlson,Jennifer R. Winquist
  • Publisher : SAGE Publications
  • Release : 09 January 2013
GET THIS BOOK An Introduction to Statistics

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 fundamental concepts. These activities are self-correcting so students discover and correct their own misunderstandings early in the learning process. This approach enables students to be responsible for their own learning

Statistical Learning Theory

Statistical Learning Theory
  • Author : Vladimir N. Vapnik,VLADIMIR AUTOR VAPNIK
  • Publisher : Wiley-Interscience
  • Release : 30 September 1998
GET THIS BOOK Statistical Learning Theory

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: Lower Bounds on the Risk of the ERM Principle. Bounds on the Risk for Real-Valued Loss Functions. The Structural Risk Minimization Principle. Appendix to Chapter 6: Estimating Functions on the Basis

Statistical Learning for Big Dependent Data

Statistical Learning for Big Dependent Data
  • Author : Daniel Peña,Ruey S. Tsay
  • Publisher : John Wiley & Sons
  • Release : 16 March 2021
GET THIS BOOK Statistical Learning for Big Dependent Data

Master advanced topics in the analysis of large, dynamically dependent datasets with this insightful resource Statistical Learning with Big Dependent Data delivers a comprehensive presentation of the statistical and machine learning methods useful for analyzing and forecasting large and dynamically dependent data sets. The book presents automatic procedures for modelling and forecasting large sets of time series data. Beginning with some visualization tools, the book discusses procedures and methods for finding outliers, clusters, and other types of heterogeneity in big

Statistical Learning with Sparsity

Statistical Learning with Sparsity
  • Author : Trevor Hastie,Robert Tibshirani,Martin Wainwright
  • Publisher : CRC Press
  • Release : 07 May 2015
GET THIS BOOK Statistical Learning with Sparsity

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 recover the underlying signal in a set of data. Top experts in this rapidly evolving field, the authors describe the lasso for linear regression and a simple coordinate descent algorithm

An Introduction to Statistical Methods and Data Analysis

An Introduction to Statistical Methods and Data Analysis
  • Author : R. Lyman Ott,Micheal T. Longnecker
  • Publisher : Cengage Learning
  • Release : 30 December 2008
GET THIS BOOK An Introduction to Statistical Methods and Data Analysis

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 research projects, to make decisions based on data in general settings both within and beyond the university setting, and to become critical readers of statistical analyses in research papers and

Neural Networks and Statistical Learning

Neural Networks and Statistical Learning
  • Author : Ke-Lin Du,M. N. S. Swamy
  • Publisher : Springer Nature
  • Release : 12 September 2019
GET THIS BOOK Neural Networks and Statistical Learning

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 working understanding of the content. This updated new edition presents recently published results and includes six new chapters that correspond to the recent advances in computational learning theory, sparse coding,