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An Introduction to Statistical Learning, James Gareth

Автор: James Gareth
Название:  An Introduction to Statistical Learning   (Джеймс Гарет: Введение в изучение статистики)
Издательство: Springer
Классификация:
Вероятность и статистика
Прикладная математика
Физика
Математическое и статистическое программное обеспечение

ISBN: 1461471370
ISBN-13(EAN): 9781461471370
ISBN: 1-461-47137-0
ISBN-13(EAN): 978-1-461-47137-0
Обложка/Формат: Hardback
Страницы: 426
Вес: 0.852 кг.
Дата издания: 25.06.2013
Серия: Springer texts in statistics
Язык: ENG
Иллюстрации: 4 black & white illustrations, 146 colour illustrations, 10 black & white tables, biography
Размер: 235 X 155 X 25
Читательская аудитория: Professional & vocational
Подзаголовок: With applications in r
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: This book presents key modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, and clustering.

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Applied Linear Statistical Models with Student CD

Автор: Nachtsheim;Neter;Kutner
Название: Applied Linear Statistical Models with Student CD
ISBN: 0071122214 ISBN-13(EAN): 9780071122214
Издательство: McGraw-Hill
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Цена: 5048 р.
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Описание: "Applied Linear Statistical Models", 5e, is the long established leading authoritative text and reference on statistical modeling. For students in most any discipline where statistical analysis or interpretation is used, ALSM serves as the standard work. The text includes brief introductory and review material, and then proceeds through regression and modeling for the first half, and through ANOVA and Experimental Design in the second half. All topics are presented in a precise and clear style supported with solved examples, numbered formulae, graphic illustrations, and "Notes" to provide depth and statistical accuracy and precision. Applications used within the text and the hallmark problems, exercises, and projects are drawn from virtually all disciplines and fields providing motivation for students in virtually any college. The Fifth edition provides an increased use of computing and graphical analysis throughout, without sacrificing concepts or rigor. In general, the 5e uses larger data sets in examples and exercises, and where methods can be automated within software without loss of understanding, it is so done.

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Pattern Recognition and Machine Learning

Автор: Bishop
Название: Pattern Recognition and Machine Learning
ISBN: 0387310738 ISBN-13(EAN): 9780387310732
Издательство: Springer
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Цена: 6244 р.
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Описание: The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications.A forthcoming companion volume will deal with practical aspects of pattern recognition and machine learning, and will include free software implementations of the key algorithms along with example data sets and demonstration programs.Christopher Bishop is Assistant Director at Microsoft Research Cambridge, and also holds a Chair in Computer Science at the University of Edinburgh. He is a Fellow of Darwin College Cambridge, and was recently elected Fellow of the Royal Academy of Engineering. The author's previous textbook "Neural Networks for Pattern Recognition" has been widely adopted.Coming soon:*For students, worked solutions to a subset of exercises available on a public web site (for exercises marked "www" in the text)*For instructors, worked solutions to remaining exercises from the Springer web site*Lecture slides to accompany each chapter*Data sets available for download

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The Elements of Statistical Learning

Автор: Trevor Hastie; Robert Tibshirani; Jerome Friedman
Название: The Elements of Statistical Learning
ISBN: 0387848576 ISBN-13(EAN): 9780387848570
Издательство: Springer
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Цена: 6156 р.
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Описание: This major new edition features many topics not covered in the original, including graphical models, random forests, and ensemble methods. As before, it covers the conceptual framework for statistical data in our rapidly expanding computerized world.

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An Introduction to Multivariate Statistical Analysis, Third Edition

Автор: T. W. Anderson
Название: An Introduction to Multivariate Statistical Analysis, Third Edition
ISBN: 0471360910 ISBN-13(EAN): 9780471360919
Издательство: Wiley
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Цена: 14586 р.
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Описание: Uses the method of maximum likelihood to a large extent to ensure reasonable, and in some cases optimal procedures. This work treats the basic and important topics in multivariate statistics.

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Introduction to Time Series Using Stata

Автор: Becketti
Название: Introduction to Time Series Using Stata
ISBN: 1597181323 ISBN-13(EAN): 9781597181327
Издательство: Taylor&Francis
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Цена: 6638 р.
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Описание: Introduction to Time Series Using Stata provides a step-by-step guide to essential timeseries techniques—from the incredibly simple to the quite complex—and, at the same time, demonstrates how these techniques can be applied in the Stata statistical package. The emphasis is on an understanding of the intuition underlying theoretical innovations and an ability to apply them. Real-world examples illustrate the application of each concept as it is introduced, and care is taken to highlight the pitfalls, as well as the power, of each new tool.

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Introduction to Probability with Mathematica, Second Edition

Автор: Hastings
Название: Introduction to Probability with Mathematica, Second Edition
ISBN: 1420079387 ISBN-13(EAN): 9781420079388
Издательство: Taylor&Francis
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Цена: 6544 р.
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Описание: Updated to conform to Mathematica® 7.0, this second edition shows how to easily create simulations from templates and solve problems using Mathematica. Along with new sections on order statistics, transformations of multivariate normal random variables, and Brownian motion, this edition offers an expanded section on Markov chains, more example data of the normal distribution, and more attention on conditional expectation. It also includes additional problems from Actuarial Exam P as well as new examples, exercises, and data sets. The accompanying CD-ROM contains updated Mathematica notebooks and a revised solutions manual is available for qualifying instructors.

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Statistical and Machine-Learning Data Mining

Автор: Ratner Bruce
Название: Statistical and Machine-Learning Data Mining
ISBN: 1439860912 ISBN-13(EAN): 9781439860915
Издательство: Taylor&Francis
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Цена: 5516 р.
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Описание: Rev. ed. of: Statistical modeling and analysis for database marketing. c2003.

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Introduction to High-Dimensional Statistics

Автор: Giraud
Название: Introduction to High-Dimensional Statistics
ISBN: 1482237946 ISBN-13(EAN): 9781482237948
Издательство: Taylor&Francis
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Цена: 4955 р.
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Описание: This is a concise guide to state-of-the-art models, techniques, and approaches for handling high-dimensional data. The book is intended to expose the reader to the key concepts and ideas in the most simple settings possible while avoiding unnecessary technicalities. This highly accessible text describes the challenges related to the analysis of high-dimensional data; covers cutting-edge statistical methods including model selection, sparsity and the lasso, aggregation, and learning theory; and provides detailed exercises at the end of every chapter with collaborative solutions on a wikisite.

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Introduction to Nonextensive Statistical Mechanics

Автор: Constantino Tsallis
Название: Introduction to Nonextensive Statistical Mechanics
ISBN: 0387853588 ISBN-13(EAN): 9780387853581
Издательство: Springer
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Цена: 7401 р.
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Описание: Metaphors, generalizations and unifications are natural and desirable ingredients of the evolution of scientific theories and concepts. This book focuses on nonextensive statistical mechanics, a generalization of Boltzmann-Gibbs (BG) statistical mechanics, one of the greatest monuments of contemporary physics.

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Introduction to Machine Learning with Applications in Information Security

Автор: Stamp
Название: Introduction to Machine Learning with Applications in Information Security
ISBN: 1138626783 ISBN-13(EAN): 9781138626782
Издательство: Taylor&Francis
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Цена: р.
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Описание: This class-tested textbook will provide in-depth coverage of the fundamentals of machine learning, with an exploration of applications in information security. The book will cover malware detection, cryptography, and intrusion detection. The book will be relevant for students in machine learning and computer security courses.

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An Introduction to Probability and Statistical Inference,

Автор: George G. Roussas
Название: An Introduction to Probability and Statistical Inference,
ISBN: 0125990200 ISBN-13(EAN): 9780125990202
Издательство: Elsevier Science
Цена: 8532 р.
Наличие на складе: Невозможна поставка.

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An Introduction to Statistical Modeling of Extreme Values

Автор: Coles Stuart
Название: An Introduction to Statistical Modeling of Extreme Values
ISBN: 1852334592 ISBN-13(EAN): 9781852334598
Издательство: Springer
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Цена: 12319 р.
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Описание: Directly oriented towards real practical application, this book develops both the basic theoretical framework of extreme value models and the statistical inferential techniques for using these models in practice. Intended for statisticians and non-statisticians alike, the theoretical treatment is elementary, with heuristics often replacing detailed mathematical proof. Most aspects of extreme modeling techniques are covered, including historical techniques (still widely used) and contemporary techniques based on point process models. A wide range of worked examples, using genuine datasets, illustrate the various modeling procedures and a concluding chapter provides a brief introduction to a number of more advanced topics, including Bayesian inference and spatial extremes. All the computations are carried out using S-PLUS, and the corresponding datasets and functions are available via the Internet for readers to recreate examples for themselves. An essential reference for students and researchers in statistics and disciplines such as engineering, finance and environmental science, this book will also appeal to practitioners looking for practical help in solving real problems. Stuart Coles is Reader in Statistics at the University of Bristol, UK, having previously lectured at the universities of Nottingham and Lancaster. In 1992 he was the first recipient of the Royal Statistical Society's research prize. He has published widely in the statistical literature, principally in the area of extreme value modeling.

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