| 000 | 04041nam a22005655i 4500 | ||
|---|---|---|---|
| 001 | 978-3-319-89803-2 | ||
| 003 | DE-He213 | ||
| 005 | 20220801213945.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 180728s2019 sz | s |||| 0|eng d | ||
| 020 |
_a9783319898032 _9978-3-319-89803-2 |
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| 024 | 7 |
_a10.1007/978-3-319-89803-2 _2doi |
|
| 050 | 4 | _aTK5101-5105.9 | |
| 072 | 7 |
_aTJK _2bicssc |
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| 072 | 7 |
_aTEC041000 _2bisacsh |
|
| 072 | 7 |
_aTJK _2thema |
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| 082 | 0 | 4 |
_a621.382 _223 |
| 245 | 1 | 0 |
_aLearning from Data Streams in Evolving Environments _h[electronic resource] : _bMethods and Applications / _cedited by Moamar Sayed-Mouchaweh. |
| 250 | _a1st ed. 2019. | ||
| 264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2019. |
|
| 300 |
_aVIII, 317 p. 131 illus., 95 illus. in color. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
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| 490 | 1 |
_aStudies in Big Data, _x2197-6511 ; _v41 |
|
| 505 | 0 | _aChapter1: Transfer Learning in Non-Stationary Environments -- Chapter2: A new combination of diversity techniques in ensemble classifiers for handling complex concept drift -- Chapter3: Analyzing and Clustering Pareto-Optimal Objects in Data Streams -- Chapter4: Error-bounded Approximation of Data Stream: Methods and Theories -- Chapter5: Ensemble Dynamics in Non-stationary Data Stream Classification -- Chapter6: Processing Evolving Social Networks for Change Detection based on Centrality Measures -- Chapter7: Large-scale Learning from Data Streams with Apache SAMOA -- Chapter8: Process Mining for Analyzing Customer Relationship Management Systems A Case Study -- Chapter9: Detecting Smooth Cluster Changes in Evolving Graph Sequences -- Chapter10: Efficient Estimation of Dynamic Density Functions with Applications in Data Streams -- Chapter11: A Survey of Methods of Incremental Support Vector Machine Learning -- Chapter12: On Social Network-based Algorithms for Data Stream Clustering. | |
| 520 | _aThis edited book covers recent advances of techniques, methods and tools treating the problem of learning from data streams generated by evolving non-stationary processes. The goal is to discuss and overview the advanced techniques, methods and tools that are dedicated to manage, exploit and interpret data streams in non-stationary environments. The book includes the required notions, definitions, and background to understand the problem of learning from data streams in non-stationary environments and synthesizes the state-of-the-art in the domain, discussing advanced aspects and concepts and presenting open problems and future challenges in this field. Provides multiple examples to facilitate the understanding data streams in non-stationary environments; Presents several application cases to show how the methods solve different real world problems; Discusses the links between methods to help stimulate new research and application directions. | ||
| 650 | 0 |
_aTelecommunication. _910437 |
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| 650 | 0 |
_aSecurity systems. _931879 |
|
| 650 | 0 |
_aData mining. _93907 |
|
| 650 | 0 |
_aControl engineering. _931970 |
|
| 650 | 1 | 4 |
_aCommunications Engineering, Networks. _931570 |
| 650 | 2 | 4 |
_aSecurity Science and Technology. _931884 |
| 650 | 2 | 4 |
_aData Mining and Knowledge Discovery. _935450 |
| 650 | 2 | 4 |
_aControl and Systems Theory. _931972 |
| 700 | 1 |
_aSayed-Mouchaweh, Moamar. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _935451 |
|
| 710 | 2 |
_aSpringerLink (Online service) _935452 |
|
| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9783319898025 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783319898049 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030078621 |
| 830 | 0 |
_aStudies in Big Data, _x2197-6511 ; _v41 _935453 |
|
| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-319-89803-2 |
| 912 | _aZDB-2-ENG | ||
| 912 | _aZDB-2-SXE | ||
| 942 | _cEBK | ||
| 999 |
_c75793 _d75793 |
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