Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track : European Conference, ECML PKDD 2021, Bilbao, Spain, September 13-17, 2021, Proceedings, Part V / edited by Yuxiao Dong, Nicolas Kourtellis, Barbara Hammer, Jose A. Lozano.

Format:

Book

Contributor:

Dong, Yuxiao, Editor.

Kourtellis, Nicolas., Editor.

Hammer, Barbara, Editor.

Lozano, José A., Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

Lecture notes in computer science. Lecture notes in artificial intelligence 2945-9141 ; 12979

Lecture Notes in Artificial Intelligence, 2945-9141 ; 12979

Language:

English

Subjects (All):

Artificial intelligence.

Social sciences-Data processing.

Computer networks.

Data mining.

Artificial Intelligence.

Computer Application in Social and Behavioral Sciences.

Computer Communication Networks.

Data Mining and Knowledge Discovery.

Local Subjects:

Artificial Intelligence.

Computer Application in Social and Behavioral Sciences.

Computer Communication Networks.

Data Mining and Knowledge Discovery.

Physical Description:

1 online resource (XXXIV, 516 pages) : 187 illustrations, 153 illustrations in color.

Edition:

1st edition 2021.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2021.

System Details:

text file PDF

Summary:

The multi-volume set LNAI 12975 until 12979 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2021, which was held during September 13-17, 2021. The conference was originally planned to take place in Bilbao, Spain, but changed to an online event due to the COVID-19 pandemic. The 210 full papers presented in these proceedings were carefully reviewed and selected from a total of 869 submissions. The volumes are organized in topical sections as follows: Research Track: Part I: Online learning; reinforcement learning; time series, streams, and sequence models; transfer and multi-task learning; semi-supervised and few-shot learning; learning algorithms and applications. Part II: Generative models; algorithms and learning theory; graphs and networks; interpretation, explainability, transparency, safety. Part III: Generative models; search and optimization; supervised learning; text mining and natural language processing; image processing, computer vision and visual analytics. Applied Data Science Track: Part IV: Anomaly detection and malware; spatio-temporal data; e-commerce and finance; healthcare and medical applications (including Covid); mobility and transportation. Part V: Automating machine learning, optimization, and feature engineering; machine learning based simulations and knowledge discovery; recommender systems and behavior modeling; natural language processing; remote sensing, image and video processing; social media.

Other Format:

Printed edition:

ISBN:

978-3-030-86517-7

9783030865177

Access Restriction:

Restricted for use by site license.