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Machine Learning and Knowledge Discovery in Databases = European Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part III /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Machine Learning and Knowledge Discovery in Databases/ edited by Frank Hutter, Kristian Kersting, Jefrey Lijffijt, Isabel Valera.
其他題名:	European Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part III /
其他作者:	Hutter, Frank.
面頁冊數:	XLIII, 755 p. 236 illus., 213 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Data mining. -
電子資源:	https://doi.org/10.1007/978-3-030-67664-3
ISBN:	9783030676643

Machine Learning and Knowledge Discovery in Databases = European Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part III /
Machine Learning and Knowledge Discovery in DatabasesEuropean Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part III /[electronic resource] :edited by Frank Hutter, Kristian Kersting, Jefrey Lijffijt, Isabel Valera. - 1st ed. 2021. - XLIII, 755 p. 236 illus., 213 illus. in color.online resource. - Lecture Notes in Artificial Intelligence ;12459. - Lecture Notes in Artificial Intelligence ;9285.

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.

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. .

ISBN: 9783030676643

Standard No.: 10.1007/978-3-030-67664-3doiSubjects--Topical Terms:

528622
Data mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

Machine Learning and Knowledge Discovery in Databases = European Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part III /
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