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Machine learning and knowledge disco...

ECML PKDD (Conference) ((2020 :)

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Machine learning and knowledge discovery in databases = European Conference, ECML PKDD 2020, Ghent, Belgium, September 14-18, 2020 : proceedings.. Part I /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine learning and knowledge discovery in databases/ edited by Frank Hutter ... [et al.].
其他題名:	European Conference, ECML PKDD 2020, Ghent, Belgium, September 14-18, 2020 : proceedings.
其他題名:	ECML PKDD 2020
其他作者:	Hutter, Frank.
團體作者:	ECML PKDD (Conference)
出版者:	Cham :Springer International Publishing : : 2021.,
面頁冊數:	l, 764 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Machine learning - Congresses. -
電子資源:	https://doi.org/10.1007/978-3-030-67658-2
ISBN:	9783030676582

Machine learning and knowledge discovery in databases = European Conference, ECML PKDD 2020, Ghent, Belgium, September 14-18, 2020 : proceedings.. Part I /
Machine learning and knowledge discovery in databasesEuropean Conference, ECML PKDD 2020, Ghent, Belgium, September 14-18, 2020 : proceedings.Part I /[electronic resource] :ECML PKDD 2020edited by Frank Hutter ... [et al.]. - Cham :Springer International Publishing :2021. - l, 764 p. :ill., digital ;24 cm. - Lecture notes in computer science,124570302-9743 ;. - Lecture notes in computer science ;12457..

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.

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: 9783030676582

Standard No.: 10.1007/978-3-030-67658-2doiSubjects--Topical Terms:

576368
Machine learning
--Congresses.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

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