Data Mining: Practical Machine Learning Tools and Techniques, Third Edition

Купить бумажную книгу и читать

Купить бумажную книгу

По кнопке выше можно купить бумажные варианты этой книги и похожих книг на сайте интернет-магазина "Лабиринт".

Using the button above you can buy paper versions of this book and similar books on the website of the "Labyrinth" online store.

Реклама. ООО "ЛАБИРИНТ.РУ", ИНН: 7728644571, erid: LatgCADz8.

Название:Data Mining: Practical Machine Learning Tools and Techniques, Third Edition

Автор:Ian H. Witten, Eibe Frank, Mark A. Hall

Издательство:Morgan Kaufmann

ISBN: 0123748569

Год: 2011

Страниц:664

Язык:Английский

Формат: PDF

Размер: 7 MB

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. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.

Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research.

*Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects *Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods *Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization.

Дата создания страницы: