Machine learning and knowledge discovery in databases : applied data science and demo track : European conference, ECML PKDD 2020, Ghent, Belgium, September 14-18, 2020 : proceedings. Part V / Yuxiao Dong, Georgiana Ifrim, Dunja Mladenić, Craig Saunders, Sofie Van Hoecke (eds.).
ECML PKDD (Conference) (2020 : Online); Dong, Yuxiao, editor.; Ifrim, Georgiana, editor.; Mladenić, Dunja, 1967- editor.; Saunders, Craig, editor.; Van Hoecke, Sofie, editor.
2021
Q325.5
Available Online

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Title Machine learning and knowledge discovery in databases : applied data science and demo track : European conference, ECML PKDD 2020, Ghent, Belgium, September 14-18, 2020 : proceedings. Part V / Yuxiao Dong, Georgiana Ifrim, Dunja Mladenić, Craig Saunders, Sofie Van Hoecke (eds.).
Meeting Name ECML PKDD (Conference) (2020 : Online)
ISBN 9783030676704 (electronic bk.)
3030676706 (electronic bk.)
9783030676711 (print)
3030676714
9783030676698
3030676692
DOI https://doi.org/10.1007/978-3-030-67670-4
Published Cham : Springer, [2021]
Language English
Description 1 online resource (xlii, 577 pages) : illustrations (chiefly color)
Other Standard Identifiers 10.1007/978-3-030-67670-4 doi
Call Number Q325.5
Dewey Decimal Classification 006.3/1
Summary The 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.
Note International conference proceedings.
Includes author index.
Access Note Access limited to authorized users.
Digital File Characteristics text file
PDF
Source of Description Online resource; title from PDF title page (SpringerLink, viewed March 23, 2021).
Added Author Dong, Yuxiao, editor.
Ifrim, Georgiana, editor.
Mladenić, Dunja, 1967- editor.
Saunders, Craig, editor.
Van Hoecke, Sofie, editor.
Series Lecture notes in computer science. Lecture notes in artificial intelligence.
Lecture notes in computer science ; 12461.
Available in Other Form Print version: 9783030676698
Print version: 9783030676711
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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.

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