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Principles and Theory for Data Mining and Machine Learning, Clarke



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Автор: Clarke
Название:  Principles and Theory for Data Mining and Machine Learning
Перевод названия: Кларк: Принципы и теория сбора данных и машинного обучения
ISBN: 9780387981345
Издательство: Springer
Классификация:
ISBN-10: 0387981349
Обложка/Формат: Hardback
Страницы: 798
Вес: 1.284 кг.
Дата издания: 01.08.2009
Серия: Springer Series in Statistics
Язык: English
Издание: 2009 ed.
Иллюстрации: 1, black & white illustrations
Размер: 241 x 156 x 59
Читательская аудитория: Postgraduate, research & scholarly
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: Extensive treatment of the most up-to-date topicsProvides the theory and concepts behind popular and emerging methodsRange of topics drawn from Statistics, Computer Science, and Electrical Engineering



Data Mining: Practical Machine Learning Tools and Techniques,

Автор: Ian H. Witten
Название: Data Mining: Practical Machine Learning Tools and Techniques,
ISBN: 0123748569 ISBN-13(EAN): 9780123748560
Издательство: Elsevier Science
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Цена: 7837 р.
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Описание: Like the popular second edition, Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. Inside, you'll learn all you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining?including both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. <br><br>Complementing the book is a fully functional platform-independent open source Weka software for machine learning, available for free download. <br><br>The book is a major revision of the second edition that appeared in 2005. While the basic core remains the same, it has been updated to reflect the changes that have taken place over the last four or five years. The highlights for the updated new edition include completely revised technique sections; new chapter on Data Transformations, new chapter on Ensemble Learning, new chapter on Massive Data Sets, a new ?book release? version of the popular Weka machine learning open source software (developed by the authors and specific to the Third Edition); new material on ?multi-instance learning?; new information on ranking the classification, plus comprehensive updates and modernization throughout. All in all, approximately 100 pages of new material.<br> <br><br>* Thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques<br><br>* Algorithmic methods at the heart of successful data mining?including tired and true methods as well as leading edge methods<br><br>* Performance improvement techniques that work by transforming the input or output<br><br>* Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization?in an updated, interactive interface. <br>

Machine Learning and Data Mining in Pattern Recognition

Автор: Perner
Название: Machine Learning and Data Mining in Pattern Recognition
ISBN: 3319419196 ISBN-13(EAN): 9783319419190
Издательство: Springer
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Цена: 13416 р.
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Описание: The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multimedia data types such as image mining, text mining, video mining and Web 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
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Цена: 8565 р.
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Описание: Rev. ed. of: Statistical modeling and analysis for database marketing. c2003.

Bayesian Reasoning and Machine Learning

Автор: Barber
Название: Bayesian Reasoning and Machine Learning
ISBN: 0521518148 ISBN-13(EAN): 9780521518147
Издательство: Cambridge Academ
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Цена: 10611 р.
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Описание: This practical introduction for final-year undergraduate and graduate students is ideally suited to computer scientists without a background in calculus and linear algebra. Numerous examples and exercises are provided. Additional resources available online and in the comprehensive software package include computer code, demos and teaching materials for instructors.

Data Mining

Автор: Roiger
Название: Data Mining
ISBN: 1498763979 ISBN-13(EAN): 9781498763974
Издательство: Taylor&Francis
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Цена: 9146 р.
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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.

Machine Learning and Data Mining in Pattern Recognition

Автор: Petra Perner
Название: Machine Learning and Data Mining in Pattern Recognition
ISBN: 3642030696 ISBN-13(EAN): 9783642030697
Издательство: Springer
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Цена: 18167 р.
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Описание: 6th International Conference MLDM 2009 Leipzig Germany July 2325 2009 Proceedings. .

Machine Learning

Автор: Mitchell
Название: Machine Learning
ISBN: 0071154671 ISBN-13(EAN): 9780071154673
Издательство: McGraw-Hill
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Цена: 10466 р.
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Описание: Covers the field of machine learning, which is the study of algorithms that allow computer programs to automatically improve through experience. This book is intended to support upper level undergraduate and introductory level graduate courses in machine learning.

Data Analysis Using Stata, Third Edition

Автор: Kohler
Название: Data Analysis Using Stata, Third Edition
ISBN: 1597181102 ISBN-13(EAN): 9781597181105
Издательство: Taylor&Francis
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Цена: 10598 р.
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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.


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