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The Elements of Statistical Learning, Trevor Hastie; Robert Tibshirani; Jerome Friedman


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Цена: 6540р.
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Склад Англия: 15 шт.  Склад Америка: 115 шт.  
При оформлении заказа до: 25 окт 2019
Ориентировочная дата поставки: конец Ноября

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Автор: Trevor Hastie; Robert Tibshirani; Jerome Friedman
Название:  The Elements of Statistical Learning   (Тревор Хасти: Элементы статистики)
Издательство: Springer
Классификация:
Вероятность и статистика
Прикладная математика
Биология и естественные науки
Биологические науки: общие вопросы
Математическая теория вычисления
Базы данных
Искусственный интеллект

ISBN: 0387848576
ISBN-13(EAN): 9780387848570
ISBN: 0-387-84857-6
ISBN-13(EAN): 978-0-387-84857-0
Обложка/Формат: Hardback
Страницы: 768
Вес: 1.39 кг.
Дата издания: 01.03.2009
Серия: Springer series in statistics
Язык: ENG
Издание: 3 rev ed
Иллюстрации: 658 black & white illustrations, biography
Размер: 23.37 x 16.05 x 3.94 cm
Читательская аудитория: Professional & vocational
Подзаголовок: Data mining, inference, and prediction
Ссылка на Издательство: Link
Рейтинг:
Поставляется из: Германии
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.



      Старое издание
The Elements of Statistical Learning

Автор: Hastie
Название: The Elements of Statistical Learning
ISBN: 0387952845 ISBN-13(EAN): 9780387952840
Издательство: Springer
Цена: 6540 р.
Наличие на складе: Невозможна поставка.
Описание: During the past decade there has been an explosion in computation and information technology. With it has 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 descibes 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 learing (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. 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 wrote much of the statistical modeling software in 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, and projection pursuit.


An Introduction to Statistical Learning

Автор: James Gareth
Название: An Introduction to Statistical Learning
ISBN: 1461471370 ISBN-13(EAN): 9781461471370
Издательство: Springer
Рейтинг:
Цена: 5609 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

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.

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
Рейтинг:
Цена: 16302 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

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 statistical relational learning

Название: Introduction to statistical relational learning
ISBN: 0262072882 ISBN-13(EAN): 9780262072885
Издательство: Wiley
Рейтинг:
Цена: 4175 р.
Наличие на складе: Поставка под заказ.

Описание: Describes advanced statistical modeling and knowledge representation techniques for an area of machine learning and probabilistic reasoning. This volume includes introductory material, tutorials for different proposed approaches, and applications.

Data Analysis Using Stata, Third Edition

Автор: Kohler
Название: Data Analysis Using Stata, Third Edition
ISBN: 1597181102 ISBN-13(EAN): 9781597181105
Издательство: Taylor&Francis
Рейтинг:
Цена: 7627 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: Data Analysis Using Stata, Third Edition is a comprehensive introduction to both statistical methods and Stata. Beginners will learn the logic of data analysis and interpretation and easily become self-sufficient data analysts. Readers already familiar with Stata will find it an enjoyable resource for picking up new tips and tricks. The book is written as a self-study tutorial and organized around examples. It interactively introduces statistical techniques such as data exploration, description, and regression techniques for continuous and binary dependent variables. Step by step, readers move through the entire process of data analysis and in doing so learn the principles of Stata, data manipulation, graphical representation, and programs to automate repetitive tasks. This third edition includes advanced topics, such as factor-variables notation, average marginal effects, standard errors in complex survey, and multiple imputation in a way, that beginners of both data analysis and Stata can understand. Using data from a longitudinal study of private households, the authors provide examples from the social sciences that are relatable to researchers from all disciplines. The examples emphasize good statistical practice and reproducible research. Readers are encouraged to download the companion package of datasets to replicate the examples as they work through the book. Each chapter ends with exercises to consolidate acquired skills.

Deterministic and Statistical Methods in Machine Learning / First International Workshop, Sheffield, UK, September 7-10, 2004. Revised Lectures

Автор: Winkler Joab, Lawrence Neil, Niranjan Mahesan
Название: Deterministic and Statistical Methods in Machine Learning / First International Workshop, Sheffield, UK, September 7-10, 2004. Revised Lectures
ISBN: 3540290737 ISBN-13(EAN): 9783540290735
Издательство: Springer
Рейтинг:
Цена: 6544 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: This book consitutes the refereed proceedings of the First International Workshop on Machine Learning held in Sheffield, UK, in September 2004.The 19 revised full papers presented were carefully reviewed and selected for inclusion in the book. They address all current issues in the rapidly maturing field of machine learning that aims to provide practical methods for data discovery, categorisation and modelling. The particular focus of the workshop was advanced research methods in machine learning and statistical signal processing.

Statistical Learning Theory and Stochastic Optimization / Ecole d`EtГ© de ProbabilitГ©s de Saint-Flour XXXI - 2001

Автор: Catoni Olivier, Picard Jean
Название: Statistical Learning Theory and Stochastic Optimization / Ecole d`EtГ© de ProbabilitГ©s de Saint-Flour XXXI - 2001
ISBN: 3540225722 ISBN-13(EAN): 9783540225720
Издательство: Springer
Рейтинг:
Цена: 4203 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: Statistical learning theory is aimed at analyzing complex data with necessarily approximate models. This book is intended for an audience with a graduate background in probability theory and statistics. It will be useful to any reader wondering why it may be a good idea, to use as is often done in practice a notoriously "wrong'' (i.e. over-simplified) model to predict, estimate or classify. This point of view takes its roots in three fields: information theory, statistical mechanics, and PAC-Bayesian theorems. Results on the large deviations of trajectories of Markov chains with rare transitions are also included. They are meant to provide a better understanding of stochastic optimization algorithms of common use in computing estimators. The author focuses on non-asymptotic bounds of the statistical risk, allowing one to choose adaptively between rich and structured families of models and corresponding estimators. Two mathematical objects pervade the book: entropy and Gibbs measures. The goal is to show how to turn them into versatile and efficient technical tools, that will stimulate further studies and results.

The Nature of Statistical Learning Theory

Автор: Vapnik
Название: The Nature of Statistical Learning Theory
ISBN: 0387987800 ISBN-13(EAN): 9780387987804
Издательство: Springer
Рейтинг:
Цена: 15427 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: Discusses the fundamental ideas which lie behind the statistical theory of learning and generalization. This book considers learning as a general problem of function estimation based on empirical data. It concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics.

Statistical learning from a regression perspective

Автор: Berk, Richard A.
Название: Statistical learning from a regression perspective
ISBN: 0387775005 ISBN-13(EAN): 9780387775005
Издательство: Springer
Рейтинг:
Цена: 12154 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: 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.

Algebraic geometry and statistical learning theory

Автор: Watanabe, Sumio
Название: Algebraic geometry and statistical learning theory
ISBN: 0521864674 ISBN-13(EAN): 9780521864671
Издательство: Cambridge Academ
Рейтинг:
Цена: 5933 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: Sure to be influential, this book lays the foundations for the use of algebraic geometry in statistical learning theory. Many widely used statistical models and learning machines applied to information science have a parameter space that is singular: mixture models, neural networks, HMMs, Bayesian networks, and stochastic context-free grammars are major examples. Algebraic geometry and singularity theory provide the necessary tools for studying such non-smooth models. Four main formulas are established: 1. the log likelihood function can be given a common standard form using resolution of singularities, even applied to more complex models; 2. the asymptotic behaviour of the marginal likelihood or 'the evidence' is derived based on zeta function theory; 3. new methods are derived to estimate the generalization errors in Bayes and Gibbs estimations from training errors; 4. the generalization errors of maximum likelihood and a posteriori methods are clarified by empirical process theory on algebraic varieties.

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
Рейтинг:
Цена: 9666 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

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.

The Elements of Statistical Learning

Автор: Hastie
Название: The Elements of Statistical Learning
ISBN: 0387952845 ISBN-13(EAN): 9780387952840
Издательство: Springer
Рейтинг:
Цена: 6540 р.
Наличие на складе: Невозможна поставка.

Описание: During the past decade there has been an explosion in computation and information technology. With it has 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 descibes 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 learing (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. 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 wrote much of the statistical modeling software in 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, and projection pursuit.

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

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


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