Taking machine learning research online with OpenML

Joaquin Vanschoren, Jan N. Rijn, Bernd Bischl
Proceedings of the 4th International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications, PMLR 41:1-4, 2015.

Abstract

OpenML is an online platform where scientists can automatically log and share machine learning data sets, code, and experiments, organize them online, and build directly on the work of others. It helps to automate many tedious aspects of research, is readily integrated into several machine learning tools, and offers easy-to-use APIs. It also enables large-scale and real-time collaboration, allowing researchers to share their very latest results, while keeping track of their impact and reuse. The combined and linked results provide a wealth of information to speed up research, assist people while analyzing data, or automate the process altogether.

Cite this Paper

BibTeX
@InProceedings{pmlr-v41-vanschoren15, title = {{Taking machine learning research online with OpenML}}, author = {Vanschoren, Joaquin and Rijn, Jan N. and Bischl, Bernd}, booktitle = {Proceedings of the 4th International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications}, pages = {1--4}, year = {2015}, editor = {Fan, Wei and Bifet, Albert and Yang, Qiang and Yu, Philip S.}, volume = {41}, series = {Proceedings of Machine Learning Research}, month = {10 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v41/vanschoren15.pdf}, url = {https://proceedings.mlr.press/v41/vanschoren15.html}, abstract = {OpenML is an online platform where scientists can automatically log and share machine learning data sets, code, and experiments, organize them online, and build directly on the work of others. It helps to automate many tedious aspects of research, is readily integrated into several machine learning tools, and offers easy-to-use APIs. It also enables large-scale and real-time collaboration, allowing researchers to share their very latest results, while keeping track of their impact and reuse. The combined and linked results provide a wealth of information to speed up research, assist people while analyzing data, or automate the process altogether.} }
Endnote
%0 Conference Paper %T Taking machine learning research online with OpenML %A Joaquin Vanschoren %A Jan N. Rijn %A Bernd Bischl %B Proceedings of the 4th International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications %C Proceedings of Machine Learning Research %D 2015 %E Wei Fan %E Albert Bifet %E Qiang Yang %E Philip S. Yu %F pmlr-v41-vanschoren15 %I PMLR %P 1--4 %U https://proceedings.mlr.press/v41/vanschoren15.html %V 41 %X OpenML is an online platform where scientists can automatically log and share machine learning data sets, code, and experiments, organize them online, and build directly on the work of others. It helps to automate many tedious aspects of research, is readily integrated into several machine learning tools, and offers easy-to-use APIs. It also enables large-scale and real-time collaboration, allowing researchers to share their very latest results, while keeping track of their impact and reuse. The combined and linked results provide a wealth of information to speed up research, assist people while analyzing data, or automate the process altogether.
RIS
TY - CPAPER TI - Taking machine learning research online with OpenML AU - Joaquin Vanschoren AU - Jan N. Rijn AU - Bernd Bischl BT - Proceedings of the 4th International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications DA - 2015/08/31 ED - Wei Fan ED - Albert Bifet ED - Qiang Yang ED - Philip S. Yu ID - pmlr-v41-vanschoren15 PB - PMLR DP - Proceedings of Machine Learning Research VL - 41 SP - 1 EP - 4 L1 - http://proceedings.mlr.press/v41/vanschoren15.pdf UR - https://proceedings.mlr.press/v41/vanschoren15.html AB - OpenML is an online platform where scientists can automatically log and share machine learning data sets, code, and experiments, organize them online, and build directly on the work of others. It helps to automate many tedious aspects of research, is readily integrated into several machine learning tools, and offers easy-to-use APIs. It also enables large-scale and real-time collaboration, allowing researchers to share their very latest results, while keeping track of their impact and reuse. The combined and linked results provide a wealth of information to speed up research, assist people while analyzing data, or automate the process altogether. ER -
APA
Vanschoren, J., Rijn, J.N. & Bischl, B.. (2015). Taking machine learning research online with OpenML. Proceedings of the 4th International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications, in Proceedings of Machine Learning Research 41:1-4 Available from https://proceedings.mlr.press/v41/vanschoren15.html.

Related Material