國立高雄大學圖資館 |

Language: English

Back

Machine learning and knowledge disco...

(1998 :)

Machine learning and knowledge discovery in databasesEuropean Conference, ECML PKDD 2020, Ghent, Belgium, September 14-18, 2020 : proceedings.Part III /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning and knowledge discovery in databasesedited by Frank Hutter ... [et al.].
Reminder of title:	European Conference, ECML PKDD 2020, Ghent, Belgium, September 14-18, 2020 : proceedings.
remainder title:	ECML PKDD 2020
other author:	Hutter, Frank.
corporate name:
Published:	Cham :Springer International Publishing :2021.
Description:	xliii, 755 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Machine learningCongresses.
Online resource:	https://doi.org/10.1007/978-3-030-67664-3
ISBN:	9783030676643$q(electronic bk.)

Machine learning and knowledge discovery in databasesEuropean 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] :ECML PKDD 2020edited by Frank Hutter ... [et al.]. - Cham :Springer International Publishing :2021. - xliii, 755 p. :ill., digital ;24 cm. - Lecture notes in computer science,124590302-9743 ;. - Lecture notes in computer science ;4891..

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$q(electronic bk.)

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

384498
Machine learning
--Congresses.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Machine learning and knowledge discovery in databasesEuropean Conference, ECML PKDD 2020, Ghent, Belgium, September 14-18, 2020 : proceedings.Part III /
LDR:03564nmm a2200373 a 4500 001 600249
003 DE-He213
005 20210224072945.0
006 m d
007 cr nn 008maaau
008 211104s2021 sz s 0 eng d
020 $a 9783030676643$q(electronic bk.)
020 $a 9783030676636$q(paper)
024 7 $a 10.1007/978-3-030-67664-3 $2 doi
035 $a 978-3-030-67664-3
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5
072 7 $a UNF $2 bicssc
072 7 $a COM021030 $2 bisacsh
072 7 $a UNF $2 thema
072 7 $a UYQE $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .E19 2020
111 2 $n (3rd : $d 1998 : $c Amsterdam, Netherlands) $3 194767
245 1 0 $a Machine learning and knowledge discovery in databases $h [electronic resource] : $b European Conference, ECML PKDD 2020, Ghent, Belgium, September 14-18, 2020 : proceedings. $n Part III / $c edited by Frank Hutter ... [et al.].
246 3 $a ECML PKDD 2020
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a xliii, 755 p. : $b ill., digital ; $c 24 cm.
490 1 $a Lecture notes in computer science, $x 0302-9743 ; $v 12459
490 1 $a Lecture notes in artificial intelligence
505 0 $a 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.
520 $a 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.
650 0 $a Machine learning $v Congresses. $3 384498
650 0 $a Data mining $v Congresses. $3 380776
650 1 4 $a Data Mining and Knowledge Discovery. $3 275288
650 2 4 $a Data Structures and Information Theory. $3 825714
650 2 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Numeric Computing. $3 275524
650 2 4 $a Computer Appl. in Social and Behavioral Sciences. $3 274376
700 1 $a Hutter, Frank. $3 841396
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Lecture notes in computer science ; $v 4891. $3 383229
830 0 $a Lecture notes in artificial intelligence. $3 822012
856 4 0 $u https://doi.org/10.1007/978-3-030-67664-3
950 $a Computer Science (SpringerNature-11645)