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1Accepted Papers at ECML/PKDD 2011 in Athens, Greece
List of Accepted Papers at ECML/PKDD 2011
13 years ago by @ecml_pkdd_2011
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2HT2010: Program
http://www.ht2010.org/Program.html
14 years ago by @dbenz
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6Anomaly Detection in Beehives: An Algorithm Comparison
P. Davidson, M. Steininger, F. Lautenschlager, A. Krause, and A. Hotho. International Conference on Sensor Networks, volume 1674 of Communications in Computer and Information Science, page 1--20. Springer, (2021)
10 months ago by @dmir
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anomaly-detection
author:KRAUSE
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acceptedanomaly-detectionauthor:KRAUSEauthor:davidsonauthor:hothoauthor:krauseauthor:lautenschlagerauthor:steiningerhoneybeesmyownresearch_imbalanced_datawe4bee
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6ConvMOS: Climate Model Output Statistics with Deep Learning
M. Steininger, D. Abel, K. Ziegler, A. Krause, H. Paeth, and A. Hotho. Data Mining and Knowledge Discovery, (Oct 27, 2022)
2 years ago by @dmir
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climate
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acceptedauthor:KRAUSEclimatefrom:msteiningermyown
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7Semi-unsupervised Learning for Time Series Classification
P. Davidson, M. Steininger, A. Huhn, A. Krause, and A. Hotho. Milets@KDD, (2022)
2 years ago by @msteininger
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myown
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timeseries
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6Ransomware Detection in Databases through Dynamic Analysis of Query Sequences
C. Sendner, L. Iffländer, S. Schindler, M. Jobst, A. Dmitrienko, and S. Kounev. (2022)
2 years ago by @csendner
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7Do Different Deep Metric Learning Losses Lead to Similar Learned Features?
K. Kobs, M. Steininger, A. Dulny, and A. Hotho. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), (October 2021)
2 years ago by @msteininger
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6ConvMOS: Climate Model Output Statistics with Deep Learning
M. Steininger, D. Abel, K. Ziegler, A. Krause, H. Paeth, and A. Hotho. Data Mining and Knowledge Discovery, (Oct 27, 2022)
2 years ago by @msteininger
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myown
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8DETECTING PRESENCE OF SPEECH IN ACOUSTIC DATA OBTAINED FROM BEEHIVES
P. Janetzky, P. Davidson, M. Steininger, A. Krause, and A. Hotho. DCASE Workshop, (2021)
2 years ago by @msteininger
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6Anomaly Detection in Beehives: An Algorithm Comparison
P. Davidson, M. Steininger, F. Lautenschlager, A. Krause, and A. Hotho. (2021)
3 years ago by @msteininger
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8Semi-unsupervised Learning: An In-depth Parameter Analysis
P. Davidson, F. Buckermann, M. Steininger, A. Krause, and A. Hotho. KI 2021: Advances in Artificial Intelligence, page 51--66. Cham, Springer International Publishing, (2021)
3 years ago by @msteininger
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6Semi-Supervised Learning for Grain Size Distribution Interpolation
K. Kobs, C. Schäfer, M. Steininger, A. Krause, R. Baumhauer, H. Paeth, and A. Hotho. Pattern Recognition. ICPR International Workshops and Challenges: Virtual Event, January 10--15, 2021, Proceedings, Part VI, page 34--44. Springer International Publishing, (2021)
3 years ago by @msteininger
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9Density-based weighting for imbalanced regression
M. Steininger, K. Kobs, P. Davidson, A. Krause, and A. Hotho. Machine Learning, 110 (8): 2187-2211 (2021)
3 years ago by @msteininger
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density
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regression
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8Deep Learning for Climate Model Output Statistics
M. Steininger, D. Abel, K. Ziegler, A. Krause, H. Paeth, and A. Hotho. NeurIPS 2020 Workshop on Tackling Climate Change with Machine Learning, (2020)
3 years ago by @msteininger
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mos
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5All the Numbers are US: Large-scale Abuse of Contact Discovery in Mobile Messengers
C. Hagen, C. Weinert, C. Sendner, A. Dmitrienko, and T. Schneider. Network and Distributed System Security Symposium (NDSS), The Internet Society, (2021)
3 years ago by @csendner
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5All the Numbers are US: Large-scale Abuse of Contact Discovery in Mobile Messengers
C. Hagen, C. Weinert, C. Sendner, A. Dmitrienko, and T. Schneider. Network and Distributed System Security Symposium (NDSS), The Internet Society, (2021)(To Appear).
4 years ago by @se-group
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5All the Numbers are US: Large-scale Abuse of Contact Discovery in Mobile Messengers
C. Hagen, C. Weinert, C. Sendner, A. Dmitrienko, and T. Schneider. Network and Distributed System Security Symposium (NDSS), The Internet Society, (2021)(To Appear).
4 years ago by @sss-group
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4Evaluating the multi-task learning approach for land use regression modelling of air pollution
A. Dulny, M. Steininger, F. Lautenschlager, A. Krause, and A. Hotho. Journal of Physics: Conference Series, 1834 (1): 012004 (March 2021)
4 years ago by @msteininger
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accepted
lur
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5All the Numbers are US: Large-scale Abuse of Contact Discovery in Mobile Messengers
C. Hagen, C. Weinert, C. Sendner, A. Dmitrienko, and T. Schneider. Network and Distributed System Security Symposium (NDSS), The Internet Society, (February 2021)
4 years ago by @sssgroup
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10Anomaly Detection in Beehives using Deep Recurrent Autoencoders
P. Davidson, M. Steininger, F. Lautenschlager, K. Kobs, A. Krause, and A. Hotho. Proceedings of the 9th International Conference on Sensor Networks (SENSORNETS 2020), 9, page 142-149. SCITEPRESS – Science and Technology Publications, Lda., (2020)
4 years ago by @msteininger
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14OpenLUR: Off-the-shelf air pollution modeling with open features and machine learning
F. Lautenschlager, M. Becker, K. Kobs, M. Steininger, P. Davidson, A. Krause, and A. Hotho. Atmospheric Environment, (2020)
4 years ago by @msteininger
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12MapLUR: Exploring a New Paradigm for Estimating Air Pollution Using Deep Learning on Map Images
M. Steininger, K. Kobs, A. Zehe, F. Lautenschlager, M. Becker, and A. Hotho. ACM Trans. Spatial Algorithms Syst., (April 2020)
4 years ago by @msteininger
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1Accepted Papers at ECML/PKDD 2011 in Athens, Greece

2HT2010: Program

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6Anomaly Detection in Beehives: An Algorithm Comparison

6ConvMOS: Climate Model Output Statistics with Deep Learning

7Semi-unsupervised Learning for Time Series Classification

6Ransomware Detection in Databases through Dynamic Analysis of Query Sequences

7Do Different Deep Metric Learning Losses Lead to Similar Learned Features?

6ConvMOS: Climate Model Output Statistics with Deep Learning

8DETECTING PRESENCE OF SPEECH IN ACOUSTIC DATA OBTAINED FROM BEEHIVES

6Anomaly Detection in Beehives: An Algorithm Comparison

8Semi-unsupervised Learning: An In-depth Parameter Analysis

6Semi-Supervised Learning for Grain Size Distribution Interpolation

9Density-based weighting for imbalanced regression

8Deep Learning for Climate Model Output Statistics

5All the Numbers are US: Large-scale Abuse of Contact Discovery in Mobile Messengers

5All the Numbers are US: Large-scale Abuse of Contact Discovery in Mobile Messengers

5All the Numbers are US: Large-scale Abuse of Contact Discovery in Mobile Messengers

4Evaluating the multi-task learning approach for land use regression modelling of air pollution

5All the Numbers are US: Large-scale Abuse of Contact Discovery in Mobile Messengers

10Anomaly Detection in Beehives using Deep Recurrent Autoencoders

14OpenLUR: Off-the-shelf air pollution modeling with open features and machine learning

12MapLUR: Exploring a New Paradigm for Estimating Air Pollution Using Deep Learning on Map Images

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