Results of the Active Learning Challenge

Isabelle Guyon, Gavin C. Cawley, Gideon Dror, Vincent Lemaire
; Active Learning and Experimental Design workshop In conjunction with AISTATS 2010, JMLR Workshop and Conference Proceedings 16:19-45, 2011.

Abstract

We organized a machine learning challenge on “active learning”, addressing problems where labeling data is expensive, but large amounts of unlabeled data are available at low cost. Examples include handwriting and speech recognition, document classification, vision tasks, drug design using recombinant molecules and protein engineering. The algorithms may place a limited number of queries to get new sample labels. The design of the challenge and its results are summarized in this paper and the best contributions made by the participants are included in these proceedings. The website of the challenge remains open as a resource for students and researchers (http://clopinet.com/al).

Cite this Paper


BibTeX
@InProceedings{pmlr-v16-guyon11a, title = {Results of the Active Learning Challenge}, author = {Isabelle Guyon and Gavin C. Cawley and Gideon Dror and Vincent Lemaire}, booktitle = {Active Learning and Experimental Design workshop In conjunction with AISTATS 2010}, pages = {19--45}, year = {2011}, editor = {Isabelle Guyon and Gavin Cawley and Gideon Dror and Vincent Lemaire and Alexander Statnikov}, volume = {16}, series = {Proceedings of Machine Learning Research}, address = {Sardinia, Italy}, month = {16 May}, publisher = {JMLR Workshop and Conference Proceedings}, pdf = {http://proceedings.mlr.press/v16/guyon11a/guyon11a.pdf}, url = {http://proceedings.mlr.press/v16/guyon11a.html}, abstract = {We organized a machine learning challenge on “active learning”, addressing problems where labeling data is expensive, but large amounts of unlabeled data are available at low cost. Examples include handwriting and speech recognition, document classification, vision tasks, drug design using recombinant molecules and protein engineering. The algorithms may place a limited number of queries to get new sample labels. The design of the challenge and its results are summarized in this paper and the best contributions made by the participants are included in these proceedings. The website of the challenge remains open as a resource for students and researchers (http://clopinet.com/al).} }
Endnote
%0 Conference Paper %T Results of the Active Learning Challenge %A Isabelle Guyon %A Gavin C. Cawley %A Gideon Dror %A Vincent Lemaire %B Active Learning and Experimental Design workshop In conjunction with AISTATS 2010 %C Proceedings of Machine Learning Research %D 2011 %E Isabelle Guyon %E Gavin Cawley %E Gideon Dror %E Vincent Lemaire %E Alexander Statnikov %F pmlr-v16-guyon11a %I PMLR %J Proceedings of Machine Learning Research %P 19--45 %U http://proceedings.mlr.press %V 16 %W PMLR %X We organized a machine learning challenge on “active learning”, addressing problems where labeling data is expensive, but large amounts of unlabeled data are available at low cost. Examples include handwriting and speech recognition, document classification, vision tasks, drug design using recombinant molecules and protein engineering. The algorithms may place a limited number of queries to get new sample labels. The design of the challenge and its results are summarized in this paper and the best contributions made by the participants are included in these proceedings. The website of the challenge remains open as a resource for students and researchers (http://clopinet.com/al).
RIS
TY - CPAPER TI - Results of the Active Learning Challenge AU - Isabelle Guyon AU - Gavin C. Cawley AU - Gideon Dror AU - Vincent Lemaire BT - Active Learning and Experimental Design workshop In conjunction with AISTATS 2010 PY - 2011/04/21 DA - 2011/04/21 ED - Isabelle Guyon ED - Gavin Cawley ED - Gideon Dror ED - Vincent Lemaire ED - Alexander Statnikov ID - pmlr-v16-guyon11a PB - PMLR SP - 19 DP - PMLR EP - 45 L1 - http://proceedings.mlr.press/v16/guyon11a/guyon11a.pdf UR - http://proceedings.mlr.press/v16/guyon11a.html AB - We organized a machine learning challenge on “active learning”, addressing problems where labeling data is expensive, but large amounts of unlabeled data are available at low cost. Examples include handwriting and speech recognition, document classification, vision tasks, drug design using recombinant molecules and protein engineering. The algorithms may place a limited number of queries to get new sample labels. The design of the challenge and its results are summarized in this paper and the best contributions made by the participants are included in these proceedings. The website of the challenge remains open as a resource for students and researchers (http://clopinet.com/al). ER -
APA
Guyon, I., Cawley, G.C., Dror, G. & Lemaire, V.. (2011). Results of the Active Learning Challenge. Active Learning and Experimental Design workshop In conjunction with AISTATS 2010, in PMLR 16:19-45

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