Predictive Data Mining Models, Olson David L., Wu Desheng
Автор: Layton Robert Название: Learning Data Mining with Python - Second Edition: Use Python to manipulate data and build predictive models ISBN: 1787126781 ISBN-13(EAN): 9781787126787 Издательство: Неизвестно Рейтинг: Цена: 60070.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Delphi Cookbook -Third Edition is a comprehensive recipe-based guide which will help you to develop your skills while working with the real business scenarios from the very beginning. This book teaches you to design and develop cross-platform applications, deploy them on the cloud platform, and leverage the extensive toolkit that Delphi provides.
Автор: Larose Daniel T Название: Data Mining and Predictive Analytics ISBN: 1118116194 ISBN-13(EAN): 9781118116197 Издательство: Wiley Рейтинг: Цена: 125610.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Learn methods of data analysis and their application to real-world data sets This updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis.
Автор: Witten, Ian H. Название: Data Mining. Practical Machine Learning Tools and Techniques, 4 ed. ISBN: 0128042915 ISBN-13(EAN): 9780128042915 Издательство: Elsevier Science Рейтинг: Цена: 61750.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches.
Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research.
Please visit the book companion website at https: //www.cs.waikato.ac.nz/ ml/weka/book.html.
It contains
Powerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the book
Online Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the book
Table of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc.
Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projects
Presents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods
Includes a downloadable WEKA software toolkit, a comprehensive collection of machine learning algorithms for data mining tasks-in an easy-to-use interactive interface
Includes open-access online courses that introduce practical applications of the material in the book
Автор: David L. Olson; Desheng Wu Название: Predictive Data Mining Models ISBN: 9811396639 ISBN-13(EAN): 9789811396632 Издательство: Springer Рейтинг: Цена: 74530.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
This book provides an overview of predictive methods demonstrated by open source software modeling with Rattle (R’) and WEKA. Knowledge management involves application of human knowledge (epistemology) with the technological advances of our current society (computer systems) and big data, both in terms of collecting data and in analyzing it. We see three types of analytic tools. Descriptive analytics focus on reports of what has happened. Predictive analytics extend statistical and/or artificial intelligence to provide forecasting capability. It also includes classification modeling. Prescriptive analytics applies quantitative models to optimize systems, or at least to identify improved systems. Data mining includes descriptive and predictive modeling. Operations research includes all three. This book focuses on prescriptive analytics.
The book seeks to provide simple explanations and demonstration of some descriptive tools. This second edition provides more examples of big data impact, updates the content on visualization, clarifies some points, and expands coverage of association rules and cluster analysis. Chapter 1 gives an overview in the context of knowledge management. Chapter 2 discusses some basic data types. Chapter 3 covers fundamentals time series modeling tools, and Chapter 4 provides demonstration of multiple regression modeling. Chapter 5 demonstrates regression tree modeling. Chapter 6 presents autoregressive/integrated/moving average models, as well as GARCH models. Chapter 7 covers the set of data mining tools used in classification, to include special variants support vector machines, random forests, and boosting.
Models are demonstrated using business related data. The style of the book is intended to be descriptive, seeking to explain how methods work, with some citations, but without deep scholarly reference. The data sets and software are all selected for widespread availability and access by any reader with computer links.
Автор: Olson David L., Wu Desheng Название: Predictive Data Mining Models ISBN: 9811396663 ISBN-13(EAN): 9789811396663 Издательство: Springer Рейтинг: Цена: 60550.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book provides an overview of predictive methods demonstrated by open source software modeling with Rattle (R`) and WEKA. Chapter 3 covers fundamentals time series modeling tools, and Chapter 4 provides demonstration of multiple regression modeling.
Автор: Finlay Steven Название: Predictive Analytics, Data Mining and Big Data ISBN: 1137379278 ISBN-13(EAN): 9781137379276 Издательство: Springer Рейтинг: Цена: 37260.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This in-depth guide provides managers with a solid understanding of data and data trends, the opportunities that it can offer to businesses, and the dangers of these technologies. Written in an accessible style, Steven Finlay provides a contextual roadmap for developing solutions that deliver benefits to organizations.
Автор: Colleen McCue Название: Data Mining and Predictive Analysis ISBN: 0128002298 ISBN-13(EAN): 9780128002292 Издательство: Elsevier Science Рейтинг: Цена: 66240.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Data Mining and Predictive Analysis: Intelligence Gathering and Crime Analysis, 2nd Edition, describes clearly and simply how crime clusters and other intelligence can be used to deploy security resources most effectively. Rather than being reactive,security agencies can anticipate and prevent crime through the appropriate application of data mining and the use of standard computer programs. Data Mining and Predictive Analysis offers a clear, practical starting point for professionals who need to use data mining inhomeland security, security analysis, and operational law enforcementsettings.This revised text highlights new and emerging technology, discusses the importance of analytic contextfor ensuring successful implementation of advanced analytics in the operational setting, and covers new analytic service delivery models that increase ease of use and access to high-end technology and analytic capabilities. The use of predictive analytics inintelligence and securityanalysis enables the development of meaningful, information based tactics, strategy, and policy decisions in the operational public safety and security environment.
Автор: Leventhal Barry Название: Predictive Analytics for Marketers: Using Data Mining for Business Advantage ISBN: 0749479930 ISBN-13(EAN): 9780749479930 Издательство: Неизвестно Рейтинг: Цена: 50790.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Understand how to apply predictive analytics to better manage a company and its resources more effectively, with this revolutionary book for marketing professionals.
Автор: S. Finlay Название: Predictive Analytics, Data Mining and Big Data ISBN: 1349478687 ISBN-13(EAN): 9781349478682 Издательство: Springer Рейтинг: Цена: 32600.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This in-depth guide provides managers with a solid understanding of data and data trends, the opportunities that it can offer to businesses, and the dangers of these technologies. Written in an accessible style, Steven Finlay provides a contextual roadmap for developing solutions that deliver benefits to organizations.
Автор: Clarke, Bertrand S. (university Of Nebraska, Lincoln) Clarke, Jennifer L. (university Of Nebraska, Lincoln) Название: Predictive statistics ISBN: 1107028280 ISBN-13(EAN): 9781107028289 Издательство: Cambridge Academ Рейтинг: Цена: 83430.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Aimed at statisticians and machine learners, this retooling of statistical theory asserts that high-quality prediction should be the guiding principle of modeling and learning from data, then shows how. The fully predictive approach to statistical problems outlined embraces traditional subfields and `black box` settings, with computed examples.
Название: Computing Predictive Analytics, Business Intelligence, and Economics ISBN: 177188729X ISBN-13(EAN): 9781771887298 Издательство: Taylor&Francis Рейтинг: Цена: 118410.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This volume brings together research and system designs that address the scientific basis and the practical systems design issues that support areas ranging from intelligent business interfaces and predictive analytics to economics modeling.
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