Machine Learning for Data Streams
Albert Bifet
From Basi6 International, Irving, TX, U.S.A.
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Add to basketFrom Basi6 International, Irving, TX, U.S.A.
Seller rating 5 out of 5 stars
AbeBooks Seller since June 24, 2016
Quantity: 6 available
Add to basketAbout this Item
New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service. Seller Inventory # ABEJUNE24-69563
Bibliographic Details
Title: Machine Learning for Data Streams
Publisher: The MIT Press
Publication Date: 2018
Binding: Hardcover
Condition: Brand New
About this title
Today many information sources—including sensor networks, financial markets, social networks, and healthcare monitoring—are so-called data streams, arriving sequentially and at high speed. Analysis must take place in real time, with partial data and without the capacity to store the entire data set. This book presents algorithms and techniques used in data stream mining and real-time analytics. Taking a hands-on approach, the book demonstrates the techniques using MOA (Massive Online Analysis), a popular, freely available open-source software framework, allowing readers to try out the techniques after reading the explanations.
The book first offers a brief introduction to the topic, covering big data mining, basic methodologies for mining data streams, and a simple example of MOA. More detailed discussions follow, with chapters on sketching techniques, change, classification, ensemble methods, regression, clustering, and frequent pattern mining. Most of these chapters include exercises, an MOA-based lab session, or both. Finally, the book discusses the MOA software, covering the MOA graphical user interface, the command line, use of its API, and the development of new methods within MOA. The book will be an essential reference for readers who want to use data stream mining as a tool, researchers in innovation or data stream mining, and programmers who want to create new algorithms for MOA.
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