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Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part II

Research output: Books and anthologies › Conference proceedings › Research

Authors

  • Ulf Brefeld (Editor)
  • Elisa Fromont (Editor)
  • Andreas Hotho (Editor)
  • Arno Knobbe (Editor)
  • Marloes Maathuis (Editor)
  • Céline Robardet (Editor)
Original languageEnglish
Place of PublicationCham
PublisherSpringer Nature Switzerland AG
Volume2
Number of pages732
ISBN (print)978-3-030-46146-1
ISBN (electronic)978-3-030-46147-8
DOIs
Publication statusPublished - 2020
EventJoint European Conference on Machine Learning and Knowledge Discovery in Databases - ECML PKDD 2019 - Würzburg, Germany
Duration: 16.09.2019 → 20.09.2019
https://ecmlpkdd2019.org/

Publication series

NameLecture notes in computer science
PublisherSpringer
Volume11906
ISSN (Print)0302-9743
ISSN (electronic)1611-3349
NameLecture Notes in Artificial Intelligence
PublisherSpringer
Volume11906
ISSN (Print)0302-9743
ISSN (electronic)1611-3349

Other publications by the same author(s)

Interactive sequential generative models for team sports

Fassmeyer, D., Cordes, M. & Brefeld, U., 02.2025, In: Machine Learning. 114, 2, 15 p., 38.

Research output: Journal contributions › Journal articles › Research › peer-review

Joint Item Response Models for Manual and Automatic Scores on Open-Ended Test Items

Bengs, D., Brefeld, U., Kroehne, U. & Zehner, F., 2025, (Accepted/In press) In: Psychometrika.

Research output: Journal contributions › Journal articles › Research › peer-review

Machine Learning and Data Mining for Sports Analytics: 11th International Workshop, MLSA 2024, Vilnius, Lithuania, September 9, 2024, Revised Selected Papers

Brefeld, U. (Editor), Davis, J. (Editor), Van Haaren, J. (Editor) & Zimmermann, A. (Editor), 2025, Cham: Springer Verlag. 119 p. (Communications in Computer and Information Science; vol. 2460)

Research output: Books and anthologies › Conference proceedings › Research

Masked autoencoder for multiagent trajectories

Rudolph, Y. & Brefeld, U., 02.2025, In: Machine Learning. 114, 2, 18 p., 44.

Research output: Journal contributions › Journal articles › Research › peer-review

The promise and challenges of computer mouse trajectories in DMHIs – A feasibility study on pre-treatment dropout predictions

Zantvoort, K., Matthiesen, J., Bjurner, P., Bendix, M., Brefeld, U., Funk, B. & Kaldo, V., 06.2025, In: Internet Interventions. 40, 7 p., 100828.

Research output: Journal contributions › Journal articles › Research › peer-review

DOI

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