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The Elements of Statistical Learning Product Detail:

  • Publisher : Springer Science & Business Media
  • Release : 11 November 2013
  • ISBN : 9780387216065
  • Page : 536 pages
  • Rating : 4.5/5 from 2 voters

The Elements of Statistical Learning Book Summary/Review:

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. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for “wide” data (p bigger than n), including multiple testing and false discovery rates. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/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, projection pursuit and gradient boosting.

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The Elements of Statistical Learning

The Elements of Statistical Learning
  • Author : Trevor Hastie,Robert Tibshirani,Jerome Friedman
  • Publisher : Springer Science & Business Media
  • Release Date : 2013-11-11
  • ISBN : 9780387216065
GET THIS BOOKThe 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

The Elements of Statistical Learning

The Elements of Statistical Learning
  • Author : R. Tibshirani,J. Friedman
  • Publisher : Unknown
  • Release Date : 2001
  • ISBN : 1489905197
GET THIS BOOKThe Elements of Statistical Learning

During the past decade there has been an explosion in computation and information technology.; With it has come a vast amount 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.;

An Introduction to Statistical Learning

An Introduction to Statistical Learning
  • Author : Gareth James,Daniela Witten,Trevor Hastie,Robert Tibshirani
  • Publisher : Springer Nature
  • Release Date : 2021-07-29
  • ISBN : 9781071614181
GET THIS BOOKAn 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, deep learning,

The Elements of Statistical Learning

The Elements of Statistical Learning
  • Author : Keith Glover
  • Publisher : Createspace Independent Publishing Platform
  • Release Date : 2016-12-05
  • ISBN : 1981129170
GET THIS BOOKThe Elements of Statistical Learning

The Elements of Statistical Learning features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorisation, and spectral clustering. There is also a chapter on methods for "wide'' data (p bigger than n), including multiple testing and false discovery rates.This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the

The Elements of Statistical Learning

The Elements of Statistical Learning
  • Author : Trevor Hastie,Robert Tibshirani,Jerome Friedman
  • Publisher : Springer
  • Release Date : 2013-07-28
  • ISBN : 1489905189
GET THIS BOOKThe 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

Outlines and Highlights for the Elements of Statistical Learning by Hastie, Isbn

Outlines and Highlights for the Elements of Statistical Learning by Hastie, Isbn
  • Author : Cram101 Textbook Reviews
  • Publisher : Academic Internet Pub Incorporated
  • Release Date : 2010-12
  • ISBN : 1617440612
GET THIS BOOKOutlines and Highlights for the Elements of Statistical Learning by Hastie, Isbn

Never HIGHLIGHT a Book Again! Virtually all of the testable terms, concepts, persons, places, and events from the textbook are included. Cram101 Just the FACTS101 studyguides give all of the outlines, highlights, notes, and quizzes for your textbook with optional online comprehensive practice tests. Only Cram101 is Textbook Specific. Accompanys: 9780387848570 .

Tietz Textbook of Laboratory Medicine - E-Book

Tietz Textbook of Laboratory Medicine - E-Book
  • Author : Nader Rifai
  • Publisher : Elsevier Health Sciences
  • Release Date : 2022-02-03
  • ISBN : 9780323834674
GET THIS BOOKTietz Textbook of Laboratory Medicine - E-Book

Use THE definitive reference for laboratory medicine and clinical pathology! Tietz Textbook of Laboratory Medicine, 7th Edition provides the guidance necessary to select, perform, and evaluate the results of new and established laboratory tests. Comprehensive coverage includes the latest advances in topics such as clinical chemistry, genetic metabolic disorders, molecular diagnostics, hematology and coagulation, clinical microbiology, transfusion medicine, and clinical immunology. From a team of expert contributors led by Nader Rifai, this reference includes access to wide-ranging online resources on

Computational Linguistics and Intelligent Text Processing

Computational Linguistics and Intelligent Text Processing
  • Author : Alexander Gelbukh
  • Publisher : Springer Science & Business Media
  • Release Date : 2010-03-18
  • ISBN : 9783642121159
GET THIS BOOKComputational Linguistics and Intelligent Text Processing

This book constitutes the proceedings of the 11th International Conference on Computational Linguistics and Intelligent Text Processing, held in Iaşi, Romania, in March 2010. The 60 paper included in the volume were carefully reviewed and selected from numerous submissions. The book also includes 3 invited papers. The topics covered are: lexical resources, syntax and parsing, word sense disambiguation and named entity recognition, semantics and dialog, humor and emotions, machine translation and multilingualism, information extraction, information retrieval, text categorization and classification, plagiarism detection,

Distributed Optimization and Statistical Learning Via the Alternating Direction Method of Multipliers

Distributed Optimization and Statistical Learning Via the Alternating Direction Method of Multipliers
  • Author : Stephen Boyd,Neal Parikh,Eric Chu
  • Publisher : Now Publishers Inc
  • Release Date : 2011
  • ISBN : 9781601984609
GET THIS BOOKDistributed Optimization and Statistical Learning Via the Alternating Direction Method of Multipliers

Surveys the theory and history of the alternating direction method of multipliers, and discusses its applications to a wide variety of statistical and machine learning problems of recent interest, including the lasso, sparse logistic regression, basis pursuit, covariance selection, support vector machines, and many others.

Information Theory and Statistical Learning

Information Theory and Statistical Learning
  • Author : Frank Emmert-Streib,Matthias Dehmer
  • Publisher : Springer Science & Business Media
  • Release Date : 2009
  • ISBN : 9780387848150
GET THIS BOOKInformation Theory and Statistical Learning

This interdisciplinary text offers theoretical and practical results of information theoretic methods used in statistical learning. It presents a comprehensive overview of the many different methods that have been developed in numerous contexts.

Neural Networks and Statistical Learning

Neural Networks and Statistical Learning
  • Author : Ke-Lin Du,M. N. S. Swamy
  • Publisher : Springer Nature
  • Release Date : 2019-09-12
  • ISBN : 9781447174523
GET THIS BOOKNeural 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,

Statistical Inference for Ergodic Diffusion Processes

Statistical Inference for Ergodic Diffusion Processes
  • Author : Yury A. Kutoyants,Jurij A. Kutojanc
  • Publisher : Springer Science & Business Media
  • Release Date : 2004
  • ISBN : 1852337591
GET THIS BOOKStatistical Inference for Ergodic Diffusion Processes

An elementary introduction to the field at the start of the book introduces a class of examples - both non-standard and classical - that reappear constantly throughout the book to illustrate the merits and demerits of the procedures as the investigation progresses. The statements of the problems are in the spirit of classical mathematical statistics, and special attention is paid to asymptotically efficient procedures."--Jacket.

A Computational Approach to Statistical Learning

A Computational Approach to Statistical Learning
  • Author : Taylor Arnold,Michael Kane,Bryan W. Lewis
  • Publisher : CRC Press
  • Release Date : 2019-01-23
  • ISBN : 9781351694766
GET THIS BOOKA Computational Approach to Statistical Learning

A Computational Approach to Statistical Learning gives a novel introduction to predictive modeling by focusing on the algorithmic and numeric motivations behind popular statistical methods. The text contains annotated code to over 80 original reference functions. These functions provide minimal working implementations of common statistical learning algorithms. Every chapter concludes with a fully worked out application that illustrates predictive modeling tasks using a real-world dataset. The text begins with a detailed analysis of linear models and ordinary least squares. Subsequent chapters

An Elementary Introduction to Statistical Learning Theory

An Elementary Introduction to Statistical Learning Theory
  • Author : Sanjeev Kulkarni,Gilbert Harman
  • Publisher : John Wiley & Sons
  • Release Date : 2011-06-09
  • ISBN : 1118023463
GET THIS BOOKAn 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

Statistical Learning from a Regression Perspective

Statistical Learning from a Regression Perspective
  • Author : Richard A. Berk
  • Publisher : Springer Science & Business Media
  • Release Date : 2008-06-14
  • ISBN : 9780387775012
GET THIS BOOKStatistical Learning from a Regression Perspective

Statistical Learning from a Regression Perspective considers statistical learning applications when interest centers on the conditional distribution of the response variable, given a set of predictors, and when it is important to characterize how the predictors are related to the response. As a first approximation, this is can be seen as an extension of nonparametric regression. Among the statistical learning procedures examined are bagging, random forests, boosting, and support vector machines. Response variables may be quantitative or categorical. Real applications