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082 0 4 _a006.312
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245 1 0 _aMachine Learning and Knowledge Discovery in Databases
_h[electronic resource] :
_bEuropean Conference, ECML PKDD 2020, Ghent, Belgium, September 14-18, 2020, Proceedings, Part I /
_cedited by Frank Hutter, Kristian Kersting, Jefrey Lijffijt, Isabel Valera.
250 _a1st ed. 2021.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2021.
300 _aL, 764 p. 219 illus., 188 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
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338 _aonline resource
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490 1 _aLecture Notes in Artificial Intelligence,
_x2945-9141 ;
_v12457
505 0 _aPattern 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.
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.
_93907
650 0 _aData structures (Computer science).
_98188
650 0 _aInformation theory.
_914256
650 0 _aMachine learning.
_91831
650 0 _aSocial sciences
_xData processing.
_983360
650 0 _aComputer vision.
_9120891
650 1 4 _aData Mining and Knowledge Discovery.
_9120892
650 2 4 _aData Structures and Information Theory.
_931923
650 2 4 _aMachine Learning.
_91831
650 2 4 _aComputer Application in Social and Behavioral Sciences.
_931815
650 2 4 _aComputer Vision.
_9120893
700 1 _aHutter, Frank.
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700 1 _aKersting, Kristian.
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700 1 _aLijffijt, Jefrey.
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700 1 _aValera, Isabel.
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710 2 _aSpringerLink (Online service)
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773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
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830 0 _aLecture Notes in Artificial Intelligence,
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856 4 0 _uhttps://doi.org/10.1007/978-3-030-67658-2
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