000 | 03457nam a22005775i 4500 | ||
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001 | 978-3-030-00734-8 | ||
003 | DE-He213 | ||
005 | 20240423125150.0 | ||
007 | cr nn 008mamaa | ||
008 | 181206s2019 sz | s |||| 0|eng d | ||
020 |
_a9783030007348 _9978-3-030-00734-8 |
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024 | 7 |
_a10.1007/978-3-030-00734-8 _2doi |
|
050 | 4 | _aQA76.9.D343 | |
072 | 7 |
_aUNF _2bicssc |
|
072 | 7 |
_aUYQE _2bicssc |
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072 | 7 |
_aCOM021030 _2bisacsh |
|
072 | 7 |
_aUNF _2thema |
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072 | 7 |
_aUYQE _2thema |
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082 | 0 | 4 |
_a006.312 _223 |
100 | 1 |
_aDing, Zhengming. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
245 | 1 | 0 |
_aLearning Representation for Multi-View Data Analysis _h[electronic resource] : _bModels and Applications / _cby Zhengming Ding, Handong Zhao, Yun Fu. |
250 | _a1st ed. 2019. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2019. |
|
300 |
_aX, 268 p. 76 illus., 69 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 |
||
490 | 1 |
_aAdvanced Information and Knowledge Processing, _x2197-8441 |
|
505 | 0 | _aIntroduction -- Multi-view Clustering with Complete Information -- Multi-view Clustering with Partial Information -- Multi-view Outlier Detection -- Multi-view Transformation Learning -- Zero-Shot Learning -- Missing Modality Transfer Learning -- Deep Domain Adaptation -- Deep Domain Generalization. . | |
520 | _aThis book equips readers to handle complex multi-view data representation, centered around several major visual applications, sharing many tips and insights through a unified learning framework. This framework is able to model most existing multi-view learning and domain adaptation, enriching readers’ understanding from their similarity, and differences based on data organization and problem settings, as well as the research goal. A comprehensive review exhaustively provides the key recent research on multi-view data analysis, i.e., multi-view clustering, multi-view classification, zero-shot learning, and domain adaption. More practical challenges in multi-view data analysis are discussed including incomplete, unbalanced and large-scale multi-view learning. Learning Representation for Multi-View Data Analysis covers a wide range of applications in the research fields of big data, human-centered computing, pattern recognition, digital marketing, web mining, and computer vision. | ||
650 | 0 | _aData mining. | |
650 | 0 | _aArtificial intelligence. | |
650 | 0 | _aPattern recognition systems. | |
650 | 1 | 4 | _aData Mining and Knowledge Discovery. |
650 | 2 | 4 | _aArtificial Intelligence. |
650 | 2 | 4 | _aAutomated Pattern Recognition. |
700 | 1 |
_aZhao, Handong. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
700 | 1 |
_aFu, Yun. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783030007331 |
776 | 0 | 8 |
_iPrinted edition: _z9783030007355 |
830 | 0 |
_aAdvanced Information and Knowledge Processing, _x2197-8441 |
|
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-030-00734-8 |
912 | _aZDB-2-SCS | ||
912 | _aZDB-2-SXCS | ||
942 | _cSPRINGER | ||
999 |
_c174970 _d174970 |