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020			_a9783030676674
024	7		_a10.1007/978-3-030-67667-4 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9.D343 _b2021 EB
245	0	0	_aMachine learning and knowledge discovery in databases : _bapplied data science track : European conference, ECML PKDD 2020, Ghent, Belgium, September 14-18, 2020 : proceedings. _pPart IV _cedited by Yuxiao Dong, Dunja Mladenić, Craig Saunders
250			_aFirst edition 2021
264		1	_aCham, Switzerland _bSpringer International Publising _c2021
300			_a1 recurso en línea (XLII, 580 páginas) _b221 ilustraciones, 197 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF _2rda
490	0		_aLecture Notes in Artificial Intelligence _v12460
505	0		_aApplied 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.
520	3		_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.
988			_aSpringer_Computer_2021
650		7	_2embne _aData mining _vCongresos y asambleas _9162648
650		7	_2embne _aAprendizaje automático _vCongresos y asambleas _9166090
700	1		_aDong, Yuxiao _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aMladenić, Dunja _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aSaunders, Craig _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
710	2		_aSpringerLink
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-67667-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b05/2021 _dz _eb _zSI