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020 _a3030676641
024 7 _a10.1007/978-3-030-67664-3
_2doi
035 _a(CKB)4100000011781544
035 _a(MiAaPQ)EBC6508435
035 _a(Au-PeEL)EBL6508435
035 _a(OCoLC)1241066181
035 _a(PPN)25385847X
035 _a(DE-He213)978-3-030-67664-3
035 _a(EXLCZ)994100000011781544
040 _aMiAaPQ
_beng
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_epn
_cMiAaPQ
_dMiAaPQ
041 0 _aeng
050 1 4 _aQ325.5
_b.M334 2021
072 7 _aUNF
_2bicssc
072 7 _aUYQE
_2bicssc
072 7 _aCOM021030
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072 7 _aUNF
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072 7 _aUYQE
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090 _aQ325.5
_b.M334 2021ebk
245 1 0 _aMachine Learning and Knowledge Discovery in Databases :
_bEuropean Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part III /
_cedited by Frank Hutter, Kristian Kersting, Jefrey Lijffijt, Isabel Valera.
250 _a1st ed. 2021.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2021.
300 _a1 online resource (783 pages).
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
490 0 _aLecture Notes in Artificial Intelligence,
_x2945-9141 ;
_v12459
505 0 _aCombinatorial optimization -- large-scale optimization and differential privacy -- boosting and ensemble methods -- Bayesian methods -- architecture of neural networks -- graph neural networks -- Gaussian processes -- computer vision and image processing -- natural language processing.-bioinformatics.
520 _aThe 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020. The conference was planned to take place in Ghent, Belgium, but had to change to an online format due to the COVID-19 pandemic. The 232 full papers and 10 demo papers presented in this volume were carefully reviewed and selected for inclusion in the proceedings. The volumes are organized in topical sections as follows: Part I: Pattern Mining; clustering; privacy and fairness; (social) network analysis and computational social science; dimensionality reduction and autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio-) temporal data and recurrent neural networks; collaborative filtering and matrix completion. Part II: deep learning optimization and theory;active learning; adversarial learning; federated learning; Kernel methods and online learning; partial label learning; reinforcement learning; transfer and multi-task learning; Bayesian optimization and few-shot learning. Part III: Combinatorial optimization; large-scale optimization and differential privacy; boosting and ensemble methods; Bayesian methods; architecture of neural networks; graph neural networks; Gaussian processes; computer vision and image processing; natural language processing; bioinformatics. Part IV: applied data science: recommendation; applied data science: anomaly detection; applied data science: Web mining; applied data science: transportation; applied data science: activity recognition; applied data science: hardware and manufacturing; applied data science: spatiotemporal data. Part V: applied data science: social good; applied data science: healthcare; applied data science: e-commerce and finance; applied data science: computational social science; applied data science: sports; demo track. .
650 0 _aData mining.
_96146
650 0 _aData structures (Computer science)
_91407
650 0 _aInformation theory.
_9579
650 0 _aArtificial intelligence.
650 0 _aNumerical analysis.
_9324
650 0 _aSocial sciences
_xData processing.
650 1 4 _aData Mining and Knowledge Discovery.
650 2 4 _aData Structures and Information Theory.
650 2 4 _aArtificial Intelligence.
650 2 4 _aNumerical Analysis.
_9324
650 2 4 _aComputer Application in Social and Behavioral Sciences.
655 7 _aElectronic books
_2local
_92032
700 1 _aHutter, Frank,
_eeditor
776 0 8 _z3-030-67663-3
830 0 _aLecture Notes in Artificial Intelligence,
_x2945-9141 ;
_v12459
856 4 0 _3Springerlink
_uhttps://link.springer.com/book/10.1007/978-3-030-67664-3
_zOnline access link to the resource
942 _2lcc
_cEBK
999 _c200467735
_d85947