The Elements of Statistical Learning

The Elements of Statistical Learning
Author: Trevor Hastie,Robert Tibshirani,Jerome Friedman
Publsiher: Springer Science & Business Media
Total Pages: 536
Release: 2013-11-11
ISBN: 0387216065
Category: Mathematics
Language: EN, FR, DE, ES & NL

The Elements of Statistical Learning Book Excerpt:

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.

The Elements of Statistical Learning

The Elements of Statistical Learning
Author: Trevor Hastie,Robert Tibshirani,Jerome H. Friedman
Publsiher: Springer Science & Business Media
Total Pages: 533
Release: 2001
ISBN: 9780387952840
Category: Mathematics
Language: EN, FR, DE, ES & NL

The Elements of Statistical Learning Book Excerpt:

This book describes the important ideas 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.

An Introduction to Statistical Learning

An Introduction to Statistical Learning
Author: Gareth James,Daniela Witten,Trevor Hastie,Robert Tibshirani
Publsiher: Springer Nature
Total Pages: 607
Release: 2021-07-29
ISBN: 1071614185
Category: Mathematics
Language: EN, FR, DE, ES & NL

An Introduction to Statistical Learning Book Excerpt:

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, survival analysis, multiple testing, 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. This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naïve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.

The Elements of Statistical Learning

The Elements of Statistical Learning
Author: Trevor Hastie,Robert Tibshirani,Jerome Friedman
Publsiher: Springer
Total Pages: 536
Release: 2013-07-28
ISBN: 9781489905185
Category: Mathematics
Language: EN, FR, DE, ES & NL

The Elements of Statistical Learning Book Excerpt:

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.

The Elements of Statistical Learning

The Elements of Statistical Learning
Author: Keith Glover
Publsiher: Createspace Independent Publishing Platform
Total Pages: 422
Release: 2016-12-05
ISBN: 9781981129171
Category: Electronic Book
Language: EN, FR, DE, ES & NL

The Elements of Statistical Learning Book Excerpt:

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 approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It is 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 Elements of Statistical Learning

The Elements of Statistical Learning
Author: R. Tibshirani,J. Friedman
Publsiher: Unknown
Total Pages: 546
Release: 2001
ISBN: 9781489905192
Category: Artificial intelligence
Language: EN, FR, DE, ES & NL

The Elements of Statistical Learning Book Excerpt:

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.; 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.

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
Publsiher: Academic Internet Pub Incorporated
Total Pages: 152
Release: 2010-12
ISBN: 9781617440618
Category: Education
Language: EN, FR, DE, ES & NL

Outlines and Highlights for the Elements of Statistical Learning by Hastie Isbn Book Excerpt:

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 .

Handbook of Statistical Bioinformatics

Handbook of Statistical Bioinformatics
Author: Henry Horng-Shing Lu,Bernhard Schölkopf,Hongyu Zhao
Publsiher: Springer Science & Business Media
Total Pages: 630
Release: 2011-05-17
ISBN: 3642163459
Category: Mathematics
Language: EN, FR, DE, ES & NL

Handbook of Statistical Bioinformatics Book Excerpt:

Numerous fascinating breakthroughs in biotechnology have generated large volumes and diverse types of high throughput data that demand the development of efficient and appropriate tools in computational statistics integrated with biological knowledge and computational algorithms. This volume collects contributed chapters from leading researchers to survey the many active research topics and promote the visibility of this research area. This volume is intended to provide an introductory and reference book for students and researchers who are interested in the recent developments of computational statistics in computational biology.

Tietz Textbook of Laboratory Medicine E Book

Tietz Textbook of Laboratory Medicine   E Book
Author: Nader Rifai
Publsiher: Elsevier Health Sciences
Total Pages: 1584
Release: 2022-02-03
ISBN: 0323834671
Category: Medical
Language: EN, FR, DE, ES & NL

Tietz Textbook of Laboratory Medicine E Book Book Excerpt:

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 Expert Consult — featuring the comprehensive product with fully searchable text, regular content updates, animations, podcasts, over 1300 clinical case studies, lecture series, and more. Authoritative, current content helps you perform tests in a cost-effective, timely, and efficient manner; provides expertise in managing clinical laboratory needs; and shows how to be responsive to an ever-changing environment. Current guidelines help you select, perform, and evaluate the results of new and established laboratory tests. Expert, internationally recognized chapter authors present guidelines representing different practices and points of view. Analytical criteria focus on the medical usefulness of laboratory procedures. Use of standard and international units of measure makes this text appropriate for any user, anywhere in the world. Expert Consult provides the entire text as a fully searchable eBook, and includes regular content updates, animations, podcasts, more than 1300 clinical case studies, over 2500 multiple-choice questions, a lecture series, and more. NEW! 19 additional chapters highlight various specialties throughout laboratory medicine. NEW! Updated, peer-reviewed content provides the most current information possible. NEW! The largest-ever compilation of clinical cases in laboratory medicine is included on Expert Consult. NEW! Over 100 adaptive learning courses on Expert Consult offer the opportunity for personalized education.

Computational Linguistics and Intelligent Text Processing

Computational Linguistics and Intelligent Text Processing
Author: Alexander Gelbukh
Publsiher: Springer Science & Business Media
Total Pages: 760
Release: 2010-03-18
ISBN: 3642121152
Category: Computers
Language: EN, FR, DE, ES & NL

Computational Linguistics and Intelligent Text Processing Book Excerpt:

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, text summarization, and speech generation.

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
Publsiher: Now Publishers Inc
Total Pages: 140
Release: 2011
ISBN: 160198460X
Category: Computers
Language: EN, FR, DE, ES & NL

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

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
Publsiher: Springer Science & Business Media
Total Pages: 439
Release: 2009
ISBN: 0387848150
Category: Computers
Language: EN, FR, DE, ES & NL

Information Theory and Statistical Learning Book Excerpt:

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
Publsiher: Springer Nature
Total Pages: 988
Release: 2019-09-12
ISBN: 1447174526
Category: Mathematics
Language: EN, FR, DE, ES & NL

Neural Networks and Statistical Learning Book Excerpt:

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, deep learning, big data and cloud computing. Each chapter features state-of-the-art descriptions and significant research findings. The topics covered include: • multilayer perceptron; • the Hopfield network; • associative memory models;• clustering models and algorithms; • t he radial basis function network; • recurrent neural networks; • nonnegative matrix factorization; • independent component analysis; •probabilistic and Bayesian networks; and • fuzzy sets and logic. Focusing on the prominent accomplishments and their practical aspects, this book provides academic and technical staff, as well as graduate students and researchers with a solid foundation and comprehensive reference on the fields of neural networks, pattern recognition, signal processing, and machine learning.

Statistical Inference for Ergodic Diffusion Processes

Statistical Inference for Ergodic Diffusion Processes
Author: Yury A. Kutoyants,Jurij A. Kutojanc
Publsiher: Springer Science & Business Media
Total Pages: 481
Release: 2004
ISBN: 9781852337599
Category: Mathematics
Language: EN, FR, DE, ES & NL

Statistical Inference for Ergodic Diffusion Processes Book Excerpt:

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
Publsiher: CRC Press
Total Pages: 362
Release: 2019-01-23
ISBN: 1351694766
Category: Business & Economics
Language: EN, FR, DE, ES & NL

A Computational Approach to Statistical Learning Book Excerpt:

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 explore extensions such as ridge regression, generalized linear models, and additive models. The second half focuses on the use of general-purpose algorithms for convex optimization and their application to tasks in statistical learning. Models covered include the elastic net, dense neural networks, convolutional neural networks (CNNs), and spectral clustering. A unifying theme throughout the text is the use of optimization theory in the description of predictive models, with a particular focus on the singular value decomposition (SVD). Through this theme, the computational approach motivates and clarifies the relationships between various predictive models. Taylor Arnold is an assistant professor of statistics at the University of Richmond. His work at the intersection of computer vision, natural language processing, and digital humanities has been supported by multiple grants from the National Endowment for the Humanities (NEH) and the American Council of Learned Societies (ACLS). His first book, Humanities Data in R, was published in 2015. Michael Kane is an assistant professor of biostatistics at Yale University. He is the recipient of grants from the National Institutes of Health (NIH), DARPA, and the Bill and Melinda Gates Foundation. His R package bigmemory won the Chamber's prize for statistical software in 2010. Bryan Lewis is an applied mathematician and author of many popular R packages, including irlba, doRedis, and threejs.

An Elementary Introduction to Statistical Learning Theory

An Elementary Introduction to Statistical Learning Theory
Author: Sanjeev Kulkarni,Gilbert Harman
Publsiher: John Wiley & Sons
Total Pages: 288
Release: 2011-06-09
ISBN: 9781118023464
Category: Mathematics
Language: EN, FR, DE, ES & NL

An Elementary Introduction to Statistical Learning Theory Book Excerpt:

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 present the basic theory behind contemporary machine learning and uniquely utilize its foundations as a framework for philosophical thinking about inductive inference. Promoting the fundamental goal of statistical learning, knowing what is achievable and what is not, this book demonstrates the value of a systematic methodology when used along with the needed techniques for evaluating the performance of a learning system. First, an introduction to machine learning is presented that includes brief discussions of applications such as image recognition, speech recognition, medical diagnostics, and statistical arbitrage. To enhance accessibility, two chapters on relevant aspects of probability theory are provided. Subsequent chapters feature coverage of topics such as the pattern recognition problem, optimal Bayes decision rule, the nearest neighbor rule, kernel rules, neural networks, support vector machines, and boosting. Appendices throughout the book explore the relationship between the discussed material and related topics from mathematics, philosophy, psychology, and statistics, drawing insightful connections between problems in these areas and statistical learning theory. All chapters conclude with a summary section, a set of practice questions, and a reference sections that supplies historical notes and additional resources for further study. An Elementary Introduction to Statistical Learning Theory is an excellent book for courses on statistical learning theory, pattern recognition, and machine learning at the upper-undergraduate and graduate levels. It also serves as an introductory reference for researchers and practitioners in the fields of engineering, computer science, philosophy, and cognitive science that would like to further their knowledge of the topic.

Statistical Learning from a Regression Perspective

Statistical Learning from a Regression Perspective
Author: Richard A. Berk
Publsiher: Springer Science & Business Media
Total Pages: 360
Release: 2008-06-14
ISBN: 0387775013
Category: Mathematics
Language: EN, FR, DE, ES & NL

Statistical Learning from a Regression Perspective Book Excerpt:

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 are emphasized, especially those with practical implications. One important theme is the need to explicitly take into account asymmetric costs in the fitting process. For example, in some situations false positives may be far less costly than false negatives. Another important theme is to not automatically cede modeling decisions to a fitting algorithm. In many settings, subject-matter knowledge should trump formal fitting criteria. Yet another important theme is to appreciate the limitation of one’s data and not apply statistical learning procedures that require more than the data can provide. The material is written for graduate students in the social and life sciences and for researchers who want to apply statistical learning procedures to scientific and policy problems. Intuitive explanations and visual representations are prominent. All of the analyses included are done in R.

Statistical Learning with Sparsity

Statistical Learning with Sparsity
Author: Trevor Hastie,Robert Tibshirani,Martin Wainwright
Publsiher: CRC Press
Total Pages: 367
Release: 2015-05-07
ISBN: 1498712177
Category: Business & Economics
Language: EN, FR, DE, ES & NL

Statistical Learning with Sparsity Book Excerpt:

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 for its computation. They discuss the application of l1 penalties to generalized linear models and support vector machines, cover generalized penalties such as the elastic net and group lasso, and review numerical methods for optimization. They also present statistical inference methods for fitted (lasso) models, including the bootstrap, Bayesian methods, and recently developed approaches. In addition, the book examines matrix decomposition, sparse multivariate analysis, graphical models, and compressed sensing. It concludes with a survey of theoretical results for the lasso. In this age of big data, the number of features measured on a person or object can be large and might be larger than the number of observations. This book shows how the sparsity assumption allows us to tackle these problems and extract useful and reproducible patterns from big datasets. Data analysts, computer scientists, and theorists will appreciate this thorough and up-to-date treatment of sparse statistical modeling.

Statistical Learning for Big Dependent Data

Statistical Learning for Big Dependent Data
Author: Daniel Peña,Ruey S. Tsay
Publsiher: John Wiley & Sons
Total Pages: 560
Release: 2021-03-02
ISBN: 1119417392
Category: Mathematics
Language: EN, FR, DE, ES & NL

Statistical Learning for Big Dependent Data Book Excerpt:

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 dependent data. It then introduces various dimension reduction methods, including regularization and factor models such as regularized Lasso in the presence of dynamical dependence and dynamic factor models. The book also covers other forecasting procedures, including index models, partial least squares, boosting, and now-casting. It further presents machine-learning methods, including neural network, deep learning, classification and regression trees and random forests. Finally, procedures for modelling and forecasting spatio-temporal dependent data are also presented. Throughout the book, the advantages and disadvantages of the methods discussed are given. The book uses real-world examples to demonstrate applications, including use of many R packages. Finally, an R package associated with the book is available to assist readers in reproducing the analyses of examples and to facilitate real applications. Analysis of Big Dependent Data includes a wide variety of topics for modeling and understanding big dependent data, like: New ways to plot large sets of time series An automatic procedure to build univariate ARMA models for individual components of a large data set Powerful outlier detection procedures for large sets of related time series New methods for finding the number of clusters of time series and discrimination methods , including vector support machines, for time series Broad coverage of dynamic factor models including new representations and estimation methods for generalized dynamic factor models Discussion on the usefulness of lasso with time series and an evaluation of several machine learning procedure for forecasting large sets of time series Forecasting large sets of time series with exogenous variables, including discussions of index models, partial least squares, and boosting. Introduction of modern procedures for modeling and forecasting spatio-temporal data Perfect for PhD students and researchers in business, economics, engineering, and science: Statistical Learning with Big Dependent Data also belongs to the bookshelves of practitioners in these fields who hope to improve their understanding of statistical and machine learning methods for analyzing and forecasting big dependent data.

Statistical Learning and Data Science

Statistical Learning and Data Science
Author: Mireille Gettler Summa,Leon Bottou,Bernard Goldfarb,Fionn Murtagh,Catherine Pardoux,Myriam Touati
Publsiher: CRC Press
Total Pages: 243
Release: 2011-12-19
ISBN: 143986764X
Category: Business & Economics
Language: EN, FR, DE, ES & NL

Statistical Learning and Data Science Book Excerpt:

Data analysis is changing fast. Driven by a vast range of application domains and affordable tools, machine learning has become mainstream. Unsupervised data analysis, including cluster analysis, factor analysis, and low dimensionality mapping methods continually being updated, have reached new heights of achievement in the incredibly rich data wor