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Machine learning for data streams : with practical examples in MOA

Author: Albert Bifet; Ricard Gavaldà; Geoffrey Holmes; Bernhard Pfahringer
Publisher: Cambridge, Massachusetts : The MIT Press, [2017]
Series: Adaptive computation and machine learning.
Edition/Format:   Print book : EnglishView all editions and formats
Summary:
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  Read more...
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Document Type: Book
All Authors / Contributors: Albert Bifet; Ricard Gavaldà; Geoffrey Holmes; Bernhard Pfahringer
ISBN: 9780262037792 0262037793
OCLC Number: 1002835809
Description: xxi, 262 pages : illustrations ; 24 cm.
Contents: Introduction --
Big Data Stream Mining --
Hands-on Introduction to MOA --
Streams and Sketches --
Dealing with Change --
Classification --
Ensemble Methods --
Regression --
Clustering --
Frequent Pattern Mining --
Introduction to MOA and its Ecosystem --
The Graphical User Interface --
Using the Command Line --
Using the API --
Developing New Methods in MOA
Series Title: Adaptive computation and machine learning.
Responsibility: Albert Bifet, Ricard Gavaldà, Geoff Holmes, Bernhard Pfahringer.

Abstract:

A hands-on approach to tasks and techniques in data stream mining and real-time analytics, with examples in MOA, a popular freely available open-source software framework.  Read more...

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