Контакты/Проезд  Доставка и Оплата Помощь/Возврат
История
  +7(495) 980-12-10
  пн-пт: 10-18 сб,вс: 11-18
  shop@logobook.ru
   
    Поиск книг                    Поиск по списку ISBN Расширенный поиск    
Найти
  Зарубежные издательства Российские издательства  
Авторы | Каталог книг | Издательства | Новинки | Учебная литература | Акции | Хиты | |
 

Statistical Learning with Sparsity, Hastie



Варианты приобретения
Цена: 16517р.
Кол-во:
Наличие: Поставка под заказ.  Есть в наличии на складе поставщика.
Склад Англия: Есть  
При оформлении заказа до:
Ориентировочная дата поставки:
При условии наличия книги у поставщика.

Добавить в корзину
в Мои желания

Автор: Hastie
Название:  Statistical Learning with Sparsity
Перевод названия: Хэсти: Статистическое изучение разреженных областей
ISBN: 9781498712163
Издательство: Taylor&Francis
Классификация:
ISBN-10: 1498712169
Обложка/Формат: Hardback
Страницы: 367
Вес: 0.812 кг.
Дата издания: 21.08.2015
Серия: Chapman & hall/crc monographs on statistics and applied probability
Язык: English
Иллюстрации: 11 tables, color; 99 illustrations, color
Размер: 244 x 163 x 22
Читательская аудитория: General (us: trade)
Ключевые слова: Probability & statistics, BUSINESS & ECONOMICS / Statistics,COMPUTERS / Machine Theory,MATHEMATICS / Probability & Statistics / General
Основная тема: Statistical Computing
Подзаголовок: The lasso and generalizations
Ссылка на Издательство: Link
Рейтинг:
Описание:

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 1 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.




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
Рейтинг:
Цена: 11478 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

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.

Handbook of Cluster Analysis

Название: Handbook of Cluster Analysis
ISBN: 1466551887 ISBN-13(EAN): 9781466551886
Издательство: Taylor&Francis
Рейтинг:
Цена: 33820 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание:

Handbook of Cluster Analysis provides a comprehensive and unified account of the main research developments in cluster analysis. Written by active, distinguished researchers in this area, the book helps readers make informed choices of the most suitable clustering approach for their problem and make better use of existing cluster analysis tools.

The book is organized according to the traditional core approaches to cluster analysis, from the origins to recent developments. After an overview of approaches and a quick journey through the history of cluster analysis, the book focuses on the four major approaches to cluster analysis. These approaches include methods for optimizing an objective function that describes how well data is grouped around centroids, dissimilarity-based methods, mixture models and partitioning models, and clustering methods inspired by nonparametric density estimation. The book also describes additional approaches to cluster analysis, including constrained and semi-supervised clustering, and explores other relevant issues, such as evaluating the quality of a cluster.

This handbook is accessible to readers from various disciplines, reflecting the interdisciplinary nature of cluster analysis. For those already experienced with cluster analysis, the book offers a broad and structured overview. For newcomers to the field, it presents an introduction to key issues. For researchers who are temporarily or marginally involved with cluster analysis problems, the book gives enough algorithmic and practical details to facilitate working knowledge of specific clustering areas.

Introduction to High-Dimensional Statistics

Автор: Giraud
Название: Introduction to High-Dimensional Statistics
ISBN: 1482237946 ISBN-13(EAN): 9781482237948
Издательство: Taylor&Francis
Рейтинг:
Цена: 9908 р.
Наличие на складе: Нет в наличии.

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.

Data Mining

Автор: Roiger
Название: Data Mining
ISBN: 1498763979 ISBN-13(EAN): 9781498763974
Издательство: Taylor&Francis
Рейтинг:
Цена: 9908 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: This textbook provides a tutorial-based introduction to data mining with a focus on model building and testing, as well as on interpreting and validating results. The book follows the format of the first edition, but with updates and additions throughout.

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
Рейтинг:
Цена: 8965 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

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.

Statistical & Machine-Learning Data

Автор: Ratner
Название: Statistical & Machine-Learning Data
ISBN: 1498797601 ISBN-13(EAN): 9781498797603
Издательство: Taylor&Francis
Рейтинг:
Цена: 17303 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: The third edition of a bestseller, Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data is still the only book, to date, to distinguish between statistical data mining and machine-learning data mining.

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: 9279 р.
Наличие на складе: Нет в наличии.

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

Estimation and Testing Under Sparsity

Автор: van de Geer
Название: Estimation and Testing Under Sparsity
ISBN: 3319327739 ISBN-13(EAN): 9783319327730
Издательство: Springer
Рейтинг:
Цена: 6886 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: Taking the Lasso method as its starting point, this book describes the main ingredients needed to study general loss functions and sparsity-inducing regularizers. It also provides a semi-parametric approach to establishing confidence intervals and tests. Sparsity-inducing methods have proven to be very useful in the analysis of high-dimensional data. Examples include the Lasso and group Lasso methods, and the least squares method with other norm-penalties, such as the nuclear norm. The illustrations provided include generalized linear models, density estimation, matrix completion and sparse principal components. Each chapter ends with a problem section. The book can be used as a textbook for a graduate or PhD course.


ООО "Логосфера " Тел:+7(495) 980-12-10 www.logobook.ru
   В Контакте     В Контакте Мед  Мобильная версия