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An Introduction to Statistical Learning, James Gareth


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Цена: 5609р.
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Склад Англия: 28 шт.  Склад Америка: 773 шт.  
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Ориентировочная дата поставки: конец Ноября

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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.



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: 6634 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

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: 6540 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.

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: 7732 р.
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Описание: Recent decades have witnessed explosive growth in new and powerful tools for timeseries analysis. These innovations have overturned older approaches to forecasting, macroeconomic policy analysis, the study of productivity and long-run economic growth, and the trading of financial assets. Familiarity with these new tools on time series is an essential skill for statisticians, econometricians, and applied researchers. 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. Sean Becketti is a financial industry veteran with three decades of experience in academics, government, and private industry. Over the last two decades, Becketti has led proprietary research teams at several leading financial firms, responsible for the models underlying the valuation, hedging, and relative value analysis of some of the largest fixed-income portfolios in the world.

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: 16302 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.

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: 5851 р.
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Описание: Ever-greater computing technologies have given rise to an exponentially growing volume of data. Today massive data sets (with potentially thousands of variables) play an important role in almost every branch of modern human activity, including networks, finance, and genetics. However, analyzing such data has presented a challenge for statisticians and data analysts and has required the development of new statistical methods capable of separating the signal from the noise. Introduction to High-Dimensional Statistics 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. Offering a succinct presentation of the mathematical foundations of high-dimensional statistics, 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 Provides detailed exercises at the end of every chapter with collaborative solutions on a wikisite Illustrates concepts with simple but clear practical examples Introduction to High-Dimensional Statistics is suitable for graduate students and researchers interested in discovering modern statistics for massive data. It can be used as a graduate text or for self-study.

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: 7863 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.

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: 5537 р.
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Описание: Introduction to Machine Learning with Applications in Information Security provides a class-tested introduction to a wide variety of machine learning algorithms, reinforced through realistic applications. The book is accessible and doesn’t prove theorems, or otherwise dwell on mathematical theory. The goal is to present topics at an intuitive level, with just enough detail to clarify the underlying concepts. The book covers core machine learning topics in-depth, including Hidden Markov Models, Principal Component Analysis, Support Vector Machines, and Clustering. It also includes coverage of Nearest Neighbors, Neural Networks, Boosting and AdaBoost, Random Forests, Linear Discriminant Analysis, Vector Quantization, Naive Bayes, Regression Analysis, Conditional Random Fields, and Data Analysis. Most of the examples in the book are drawn from the field of information security, with many of the machine learning applications specifically focused on malware. The applications presented are designed to demystify machine learning techniques by providing straightforward scenarios. Many of the exercises in this book require some programming, and basic computing concepts are assumed in a few of the application sections. However, anyone with a modest amount of programming experience should have no trouble with this aspect of the book. Instructor resources, including PowerPoint slides, lecture videos, and other relevant materialare provided on an accompanying website: http://www.cs.sjsu.edu/~stamp/ML/. For the reader’s benefit, the figures in the book are also available in electronic form, and in color. About the Author Mark Stamp has been a Professor of Computer Science at San Jose State University since 2002. Prior to that, he worked at the National Security Agency (NSA) for seven years, and a Silicon Valley startup company for two years. He received his Ph.D. from Texas Tech University in 1992. His love affair with machine learning began in the early 1990s, when he was working at the NSA, and continues today at SJSU, where he has supervised vast numbers of master’s student projects, most of which involve a combination of information security and machine learning.

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: 7314 р.
Наличие на складе: Поставка под заказ.

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.

Elementary introduction to statistical learning theory

Автор: Kulkarni, Sanjeev Harman, Gilbert
Название: Elementary introduction to statistical learning theory
ISBN: 0470641835 ISBN-13(EAN): 9780470641835
Издательство: Wiley
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Цена: 9666 р.
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Описание: * Serves as a fundamental introduction to statistical learning theory and its role in understanding human learning and inductive reasoning. * Topics of coverage include: probability, pattern recognition, optimal Bayes decision rule, nearest neighbor rule, kernel rules, neural networks, and support vector machines.

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: 5642 р.
Наличие на складе: Поставка под заказ.

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.

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: 6164 р.
Наличие на складе: Поставка под заказ.

Описание: Rev. ed. of: Statistical modeling and analysis for database marketing. c2003.

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: 9065 р.
Наличие на складе: Невозможна поставка.


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