Machine learning and knowledge discovery in databases : European conference, ECML PKDD 2020, Ghent, Belgium, September 14-18, 2020 : proceedings. Part II / Frank Hutter, Kristian Kersting, Jefrey Lijffijt, Isabel Valera (eds.).
2021
Q325.5
Linked e-resources
Linked Resource
Online Access
Concurrent users
Unlimited
Authorized users
Authorized users
Document Delivery Supplied
Can lend chapters, not whole ebooks
Details
Title
Machine learning and knowledge discovery in databases : European conference, ECML PKDD 2020, Ghent, Belgium, September 14-18, 2020 : proceedings. Part II / Frank Hutter, Kristian Kersting, Jefrey Lijffijt, Isabel Valera (eds.).
Meeting Name
ECML PKDD (Conference) (2020 : Online)
ISBN
9783030676612 (electronic bk.)
3030676617 (electronic bk.)
9783030676605
3030676617 (electronic bk.)
9783030676605
Published
Cham : Springer, [2021]
Language
English
Description
1 online resource (xliii, 742 pages) : illustrations (chiefly color)
Item Number
10.1007/978-3-030-67661-2 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.
Includes author index.
Access Note
Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed March 23, 2021).
Added Author
Hutter, Frank, editor.
Kersting, Kristian, editor.
Lijffijt, Jefrey, editor.
Valera, Isabel, editor.
Kersting, Kristian, editor.
Lijffijt, Jefrey, editor.
Valera, Isabel, editor.
Series
Lecture notes in computer science. Lecture notes in artificial intelligence.
Lecture notes in computer science ; 12458.
Lecture notes in computer science ; 12458.
Available in Other Form
Print version: 9783030676605
Print version: 9783030676629
Print version: 9783030676629
Linked Resources
Online Access
Record Appears in
Online Resources > Ebooks
All Resources
All Resources
Table of Contents
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.
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.