The Elements of Statistical Learning Book
Score: 4.5
From 2 Ratings

The Elements of Statistical Learning


  • Author : Trevor Hastie
  • Publisher : Springer Science & Business Media
  • Release Date : 2013-11-11
  • Genre: Mathematics
  • Pages : 536
  • ISBN 10 : 9780387216065

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The Elements of Statistical Learning Book Description :

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-inven

The Elements of Statistical Learning Book
Score: 4
From 4 Ratings

The Elements of Statistical Learning


  • Author : Trevor Hastie
  • Publisher : Springer Science & Business Media
  • Release Date : 2009-08-26
  • Genre: Computers
  • Pages : 745
  • ISBN 10 : 0387848584

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The Elements of Statistical Learning Book Description :

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 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 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.

The Elements of Statistical Learning Book
Score: 4.5
From 14 Ratings

The Elements of Statistical Learning


  • Author : Trevor Hastie
  • Publisher : Springer Science & Business Media
  • Release Date : 2001
  • Genre: Mathematics
  • Pages : 533
  • ISBN 10 : 0387952845

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The Elements of Statistical Learning Book Description :

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.

The Elements of Statistical Learning Book
Score: 5
From 1 Ratings

The Elements of Statistical Learning


  • Author : Trevor Hastie
  • Publisher :
  • Release Date : 2001
  • Genre: Supervised learning (Machine learning)
  • Pages : 533
  • ISBN 10 : 7506292319

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The Elements of Statistical Learning Book Description :

An Introduction to Statistical Learning Book
Score: 5
From 2 Ratings

An Introduction to Statistical Learning


  • Author : Gareth James
  • Publisher : Springer Science & Business Media
  • Release Date : 2013-06-24
  • Genre: Mathematics
  • Pages : 426
  • ISBN 10 : 9781461471387

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An Introduction to Statistical Learning Book Description :

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.

Generalizability Theory Book

Generalizability Theory


  • Author : Robert L. Brennan
  • Publisher : Springer Science & Business Media
  • Release Date : 2013-11-11
  • Genre: Social Science
  • Pages : 538
  • ISBN 10 : 9781475734560

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Generalizability Theory Book Description :

Generalizability theory offers an extensive conceptual framework and a powerful set of statistical procedures for characterizing and quantifying the fallibility of measurements. Robert Brennan, the author, has written the most comprehensive and up-to-date treatment of generalizability theory. The book provides a synthesis of those parts of the statistical literature that are directly applicable to generalizability theory. The principal intended audience is measurement practitioners and graduate students in the behavioral and social sciences, although a few examples and references are provided from other fields. Readers will benefit from some familiarity with classical test theory and analysis of variance, but the treatment of most topics does not presume specific background.

An Introduction to the Bootstrap Book
Score: 4.5
From 6 Ratings

An Introduction to the Bootstrap


  • Author : Bradley Efron
  • Publisher : CRC Press
  • Release Date : 1994-05-15
  • Genre: Mathematics
  • Pages : 456
  • ISBN 10 : 0412042312

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An Introduction to the Bootstrap Book Description :

Statistics is a subject of many uses and surprisingly few effective practitioners. The traditional road to statistical knowledge is blocked, for most, by a formidable wall of mathematics. The approach in An Introduction to the Bootstrap avoids that wall. It arms scientists and engineers, as well as statisticians, with the computational techniques they need to analyze and understand complicated data sets.

Statistical Learning with Sparsity Book

Statistical Learning with Sparsity


  • Author : Trevor Hastie
  • Publisher : CRC Press
  • Release Date : 2015-05-07
  • Genre: Business & Economics
  • Pages : 367
  • ISBN 10 : 9781498712170

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Statistical Learning with Sparsity Book Description :

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.

Statistics Book

Statistics


  • Author : Robert S. Witte
  • Publisher : Wiley Global Education
  • Release Date : 2016-12-21
  • Genre: Psychology
  • Pages : 496
  • ISBN 10 : 9781119299110

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Statistics Book Description :

Drawing upon over 40 years of experience, the authors of Statistics, 11th Edition provide students with a clear and methodical approach to essential statistical procedures. The text clearly explains the basic concepts and procedures of descriptive and inferential statistical analysis. It features an emphasis on expressions involving sums of squares and degrees of freedom as well as a strong stress on the importance of variability. This accessible approach will help students tackle such perennially mystifying topics as the standard deviation, variance interpretation of the correlation coefficient, hypothesis tests, degrees of freedom, p-values, and estimates of effect size.

The Nature of Statistical Learning Theory Book
Score: 4
From 7 Ratings

The Nature of Statistical Learning Theory


  • Author : Vladimir Vapnik
  • Publisher : Springer Science & Business Media
  • Release Date : 1999-11-19
  • Genre: Mathematics
  • Pages : 314
  • ISBN 10 : 0387987800

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The Nature of Statistical Learning Theory Book Description :

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 readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists.

Machine Learning Book

Machine Learning


  • Author : Kevin P. Murphy
  • Publisher : MIT Press
  • Release Date : 2012-08-24
  • Genre: Computers
  • Pages : 1067
  • ISBN 10 : 9780262018029

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Machine Learning Book Description :

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

Applied Predictive Modeling Book
Score: 5
From 1 Ratings

Applied Predictive Modeling


  • Author : Max Kuhn
  • Publisher : Springer Science & Business Media
  • Release Date : 2013-05-17
  • Genre: Medical
  • Pages : 600
  • ISBN 10 : 9781461468493

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Applied Predictive Modeling Book Description :

Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. The text illustrates all parts of the modeling process through many hands-on, real-life examples, and every chapter contains extensive R code for each step of the process. This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book’s R package. This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics.

Statistical Learning from a Regression Perspective Book

Statistical Learning from a Regression Perspective


  • Author : Richard A. Berk
  • Publisher : Springer Nature
  • Release Date : 2020-06-29
  • Genre: Mathematics
  • Pages : 433
  • ISBN 10 : 9783030401894

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Statistical Learning from a Regression Perspective Book Description :

This textbook considers statistical learning applications when interest centers on the conditional distribution of a response variable, given a set of predictors, and in the absence of a credible model that can be specified before the data analysis begins. Consistent with modern data analytics, it emphasizes that a proper statistical learning data analysis depends in an integrated fashion on sound data collection, intelligent data management, appropriate statistical procedures, and an accessible interpretation of results. The unifying theme is that supervised learning properly can be seen as a form of regression analysis. Key concepts and procedures are illustrated with a large number of real applications and their associated code in R, with an eye toward practical implications. The growing integration of computer science and statistics is well represented including the occasional, but salient, tensions that result. Throughout, there are links to the big picture. The third edition considers significant advances in recent years, among which are: the development of overarching, conceptual frameworks for statistical learning; the impact of “big data” on statistical learning; the nature and consequences of post-model selection statistical inference; deep learning in various forms; the special challenges to statistical inference posed by statistical learning; the fundamental connections between data collection and data analysis; interdisciplinary ethical and political issues surrounding the application of algorithmic methods in a wide variety of fields, each linked to concerns about transparency, fairness, and accuracy. This edition features new sections on accuracy, transparency, and fairness, as well as a new chapter on deep learning. Precursors to deep learning get an expanded treatment. The connections between fitting and forecasting are considered in greater depth. Discussion of the estimation targets for algorithmic methods is revised and expanded throughout to

Additive Logistic Regression Book

Additive Logistic Regression


  • Author : Jerome H. Friedman
  • Publisher :
  • Release Date : 1998
  • Genre: Regression analysis
  • Pages : 45
  • ISBN 10 : OCLC:40665647

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Additive Logistic Regression Book Description :

An Elementary Introduction to Statistical Learning Theory Book
Score: 5
From 1 Ratings

An Elementary Introduction to Statistical Learning Theory


  • Author : Sanjeev Kulkarni
  • Publisher : John Wiley & Sons
  • Release Date : 2011-06-09
  • Genre: Mathematics
  • Pages : 288
  • ISBN 10 : 1118023463

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An Elementary Introduction to Statistical Learning Theory Book Description :

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 stati

All of Statistics Book
Score: 4
From 1 Ratings

All of Statistics


  • Author : Larry Wasserman
  • Publisher : Springer Science & Business Media
  • Release Date : 2013-12-11
  • Genre: Mathematics
  • Pages : 442
  • ISBN 10 : 9780387217369

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All of Statistics Book Description :

Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.

A Computational Approach to Statistical Learning Book

A Computational Approach to Statistical Learning


  • Author : Taylor Arnold
  • Publisher : CRC Press
  • Release Date : 2019-01-23
  • Genre: Business & Economics
  • Pages : 362
  • ISBN 10 : 9781351694766

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A Computational Approach to Statistical Learning Book Description :

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 man

Neural Networks and Statistical Learning Book

Neural Networks and Statistical Learning


  • Author : Ke-Lin Du
  • Publisher : Springer Nature
  • Release Date : 2019-09-12
  • Genre: Mathematics
  • Pages : 988
  • ISBN 10 : 9781447174523

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Neural Networks and Statistical Learning Book Description :

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.

Computer Age Statistical Inference Book

Computer Age Statistical Inference


  • Author : Bradley Efron
  • Publisher : Cambridge University Press
  • Release Date : 2016-07-20
  • Genre: Business & Economics
  • Pages : 475
  • ISBN 10 : 9781107149892

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Computer Age Statistical Inference Book Description :

Take an exhilarating journey through the modern revolution in statistics with two of the ringleaders.