| 000 | 02269nam a22002297a 4500 | ||
|---|---|---|---|
| 999 |
_c3845 _d3845 |
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| 005 | 20221122110832.0 | ||
| 008 | 221122b ||||| |||| 00| 0 eng d | ||
| 020 | _a9780262037792 | ||
| 082 |
_a006.312 _bBIF |
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| 100 |
_aBifet, Albert _99067 |
||
| 245 |
_aMachine learning for data streams: _bwith practical examples in MOA |
||
| 260 |
_bThe MIT press _aCambridge _c2017 |
||
| 300 | _axxi, 262 p. | ||
| 365 |
_aUSD _b55.00 |
||
| 520 | _aA 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. 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. | ||
| 650 |
_aMachine learning _92343 |
||
| 650 |
_aNeural networks (Computer science) _92344 |
||
| 650 |
_aDatabase management _910195 |
||
| 700 |
_aGavalda, Ricard _910196 |
||
| 700 |
_aHolmes, Geoffrey _910197 |
||
| 942 |
_2ddc _cBK |
||