Description: Machine Learning for Data Streams : With Practical Examples in Moa, Paperback by Bifet, Albert; Gavalda, Ricard; Holmes, Geoffrey; Pfahringer, Bernhard, ISBN 026254783X, ISBN-13 9780262547833, Like New Used, Free shipping in the US 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, th 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. Th 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, th 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. Th 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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Book Title: Machine Learning for Data Streams : With Practical Examples in Mo
Number of Pages: 288 Pages
Language: English
Publication Name: Machine Learning for Data Streams : with Practical Examples in Moa
Publisher: MIT Press
Publication Year: 2023
Subject: Intelligence (Ai) & Semantics, General, Databases / Data Mining
Type: Textbook
Item Weight: 13 Oz
Subject Area: Mathematics, Computers
Item Length: 9 in
Author: Bernhard Pfahringer, Albert Bifet, Geoffrey Holmes, Ricard Gavalda
Item Width: 7 in
Series: Adaptive Computation and Machine Learning Ser.
Format: Trade Paperback
