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Machine Learning for Data Streams with Practical Examples in MOA (Adaptive Computation and Machine Learning series) - Albert Bifet, Ricard Gavalda, Geoff Holmes 2017 PDF The MIT Press BOOKS OS AND DB
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Machine Learning for Data Streams with Practical Examples in MOA (Adaptive Computation and Machine Learning series)
Author: Albert Bifet, Ricard Gavalda, Geoff Holmes
Year: 2017
Number of pages: 287
Format: PDF
File size: 14.9 MB
Language: ENG

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.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.

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