Machine Learning for Data Streams: Practical Examples in MOA (Massive Online Analysis)
Overview & Synopsis
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.
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Document Information
| Document Title | Machine Learning for Data Streams: Practical Examples in MOA (Massive Online Analysis) |
|---|---|
| Author / Organization | Albert Bifet, Ricard Gavalda, Geoff Holmes, Bernhard Pfahringer |
| Subject & Discipline | Machine Learning, Data Analysis and Data Mining, Big Data and Data Stream, Data Science, Algorithms and Data Structures, Statistics, Mathematical Statistics, and SAS Programming, Probability and Stochastic Process |
| Categories | Machine Learning |
| ISBN | 0262037793 |
| License / Rights | Creative Commons License (CC) |
| Resource Verification | Access Restricted / Protected Host |
| Original Host Website |
FreeComputerBooks.com
(freecomputerbooks.com)
|
| File Format | application/pdf (Portable Document Format) |
| Language | ENGLISH |
| Catalog ID | DOC-261536 |
| Original Source Link | Visit Source Page on freecomputerbooks.com » |