利用 OpenML 将机器学习研究推向线上

Joaquin Vanschoren、Jan N. Rijn、Bernd Bischl
第四届大数据、流数据和异构源挖掘国际研讨会：算法、系统、编程模型和应用, PMLR 41:1-4, 2015.

摘要

OpenML 是一个在线平台，科学家可以在该平台上自动记录和分享机器学习数据集、代码和实验，在线组织它们，并直接基于他人的工作进行构建。它有助于自动化许多繁琐的研究方面，可以轻松集成到多种机器学习工具中，并提供易于使用的 API。它还能够进行大规模和实时的协作，允许研究人员分享他们最新的结果，同时跟踪其影响和复用情况。组合和链接的结果提供了大量信息，可以加速研究，帮助人们分析数据，或完全自动化该过程。

引用本文

BibTeX
@InProceedings{pmlr-v41-vanschoren15, title = {{利用 OpenML 将机器学习研究推向线上}}, 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 = {https://pmlr.com.cn/v41/vanschoren15.pdf}, url = {https://pmlr.com.cn/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 会议论文 %T 利用 OpenML 将机器学习研究推向线上 %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://pmlr.com.cn/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 - 利用 OpenML 将机器学习研究推向线上 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 - https://pmlr.com.cn/v41/vanschoren15.pdf UR - https://pmlr.com.cn/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). 利用 OpenML 将机器学习研究推向线上. 第四届大数据、流数据和异构源挖掘国际研讨会：算法、系统、编程模型和应用, in Proceedings of Machine Learning Research 41:1-4 可从 https://pmlr.com.cn/v41/vanschoren15.html 获取.

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