MARC details
| 000 -LEADER |
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| fixed length control field |
02269nam a22002297a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20221122110832.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
221122b ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9780262037792 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
006.312 |
| Item number |
BIF |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Bifet, Albert |
| 245 ## - TITLE STATEMENT |
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| Title |
Machine learning for data streams: |
| Remainder of title |
with practical examples in MOA |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Name of publisher, distributor, etc. |
The MIT press |
| Place of publication, distribution, etc. |
Cambridge |
| Date of publication, distribution, etc. |
2017 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
xxi, 262 p. |
| 365 ## - TRADE PRICE |
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| Price type code |
USD |
| Price amount |
55.00 |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
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.<br/><br/>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.<br/><br/>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 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Machine learning |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Neural networks (Computer science) |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Database management |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Gavalda, Ricard |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Holmes, Geoffrey |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Book |