Machine Learning and Knowledge Discovery in Databases. Research Track | European Conference, ECML PKDD 2021, Bilbao, Spain, September 13-17, 2021, Proceedings, Part I

Nuria Oliver (u. a.)

ISBN 10: 3030864855 / ISBN 13: 9783030864859

Published by Springer, 2021

About this Item

Machine Learning and Knowledge Discovery in Databases. Research Track | European Conference, ECML PKDD 2021, Bilbao, Spain, September 13-17, 2021, Proceedings, Part I | Nuria Oliver (u. a.) | Taschenbuch | Lecture Notes in Computer Science | xli | Englisch | 2021 | Springer | EAN 9783030864859 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Seller Inventory # 120424323

Bibliographic Details

• Title: Machine Learning and Knowledge Discovery in Databases. Research Track | European Conference, ECML PKDD 2021, Bilbao, Spain, September 13-17, 2021, Proceedings, Part I
• Publisher: Springer
• Publication Date: 2021
• Language: English
• ISBN 10: 3030864855
• ISBN 13: 9783030864859
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 46D millimeters 235 millimeters width by 155 millimeters height by 46 millimeters depth
• Weight: 1258 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

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.