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Peter Prettenhofer
Peter Prettenhofer
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Cited by
Year
Scikit-learn: Machine learning in Python
F Pedregosa, G Varoquaux, A Gramfort, V Michel, B Thirion, O Grisel, ...
the Journal of machine Learning research 12, 2825-2830, 2011
937732011
API design for machine learning software: experiences from the scikit-learn project
L Buitinck, G Louppe, M Blondel, F Pedregosa, A Mueller, O Grisel, ...
arXiv preprint arXiv:1309.0238, 2013
34692013
Scikit‐learn: Machine learning in python Fabian
F Pedregosa
Journal of machine learning research 12, 2825, 2011
20402011
Cross-language text classification using structural correspondence learning
P Prettenhofer, B Stein
Proceedings of the 48th annual meeting of the association for computational …, 2010
3992010
Intrinsic plagiarism analysis
B Stein, N Lipka, P Prettenhofer
Language Resources and Evaluation 45, 63-82, 2011
2282011
Scikit-learn: Machine learning in python journal of machine learning research
F Pedregosa, G Varoquaux, A Gramfort, V Michel, B Thirion, O Grisel, ...
Journal of machine learning research 12, 2825-2830, 2011
1972011
Cross-lingual adaptation using structural correspondence learning
P Prettenhofer, B Stein
ACM Transactions on Intelligent Systems and Technology (TIST) 3 (1), 1-22, 2011
1032011
Duchesnay
F Pedregosa, G Varoquaux, A Gramfort, V Michel, B Thirion, O Grisel, ...
E.: Scikit-learn: Machine learning in Python. JMLR 12, 2825-2830, 2011
902011
Gradient boosted regression trees in scikit-learn
P Prettenhofer, G Louppe
PyData 2014, 2014
812014
Scikit-learn: machine learning in Python. arXiv 2012
F Pedregosa, G Varoquaux, A Gramfort, V Michel, B Thirion, O Grisel, ...
arXiv preprint arXiv:1201.0490, 0
53
Scikit-learn: Machine learning in Python
L Buitinck, G Louppe, M Blondel, F Pedregosa, A Mueller, O Grisel, ...
Journal of machine learning research 12 (85), 2825-2830, 2011
502011
and J. Vanderplas
F Pedregosa, G Varoquaux, A Gramfort, V Michel, B Thirion, O Grisel
Scikit-learn: Machine learning in Python. The Journal of machine Learning …, 2011
482011
Different degrees of explicitness in intentional artifacts-studying user goals in a large search query log
M Strohmaier, P Prettenhofer, M Lux
Proceedings of the CSKGOI 8, 2008
202008
scikit-learn/scikit-learn: Scikit-learn 0.22. 1
O Grisel, A Mueller, A Gramfort, G Louppe, P Prettenhofer, M Blondel, ...
Zenodo, 2020
142020
Efficient statement identification for automatic market forecasting
H Wachsmuth, P Prettenhofer, B Stein
Proceedings of the 23rd International Conference on Computational …, 2010
122010
Acquiring explicit user goals from search query logs
M Strohmaier, P Prettenhofer, M Kröll
2008 IEEE/WIC/ACM International Conference on Web Intelligence and …, 2008
112008
Dubourg VJJomlr
F Pedregosa, G Varoquaux, A Gramfort, V Michel, B Thirion, O Grisel, ...
Scikit-learn: Machine learning in Python 12, 2825-30, 2011
72011
scikit-learn/scikit-learn: scikit-learn 1.0. 1
O Grisel, A Mueller, A Gramfort, G Louppe, P Prettenhofer, M Blondel, ...
Zenodo, 2022
32022
Forecasting daily solar energy production using robust regression techniques
G Louppe, P Prettenhofer
94th American Meteorological Society Annual Meeting, 2014
22014
Logistic regression
F Pedregosa, G Varoquaux, A Gramfort, V Michel, B Thirion, O Grisel, ...
J. Mach. Learn. Res 12, 2825-2830, 2011
22011
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