Active Learning with Clustering

Zalán Bodó, Zsolt Minier, Lehel Csató
Active Learning and Experimental Design workshop In conjunction with AISTATS 2010, PMLR 16:127-139, 2011.

Abstract

Active learning is an important field of machine learning and it is becoming more widely used in case of problems where labeling the examples in the training data set is expensive. In this paper we present a clustering-based algorithm used in the Active Learning Challenge (http://www.causality.inf.ethz.ch/activelearning.php). The algorithm is based on graph clustering with normalized cuts, and uses k-means to extract representative points from the data and approximate spectral clustering for efficiently performing the computations.

Cite this Paper

BibTeX
@InProceedings{pmlr-v16-bodo11a, title = {Active Learning with Clustering}, author = {Bodó, Zalán and Minier, Zsolt and Csató, Lehel}, booktitle = {Active Learning and Experimental Design workshop In conjunction with AISTATS 2010}, pages = {127--139}, year = {2011}, editor = {Guyon, Isabelle and Cawley, Gavin and Dror, Gideon and Lemaire, Vincent and Statnikov, Alexander}, volume = {16}, series = {Proceedings of Machine Learning Research}, address = {Sardinia, Italy}, month = {16 May}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v16/bodo11a/bodo11a.pdf}, url = {https://proceedings.mlr.press/v16/bodo11a.html}, abstract = {Active learning is an important field of machine learning and it is becoming more widely used in case of problems where labeling the examples in the training data set is expensive. In this paper we present a clustering-based algorithm used in the Active Learning Challenge (http://www.causality.inf.ethz.ch/activelearning.php). The algorithm is based on graph clustering with normalized cuts, and uses k-means to extract representative points from the data and approximate spectral clustering for efficiently performing the computations.} }
Endnote
%0 Conference Paper %T Active Learning with Clustering %A Zalán Bodó %A Zsolt Minier %A Lehel Csató %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-bodo11a %I PMLR %P 127--139 %U https://proceedings.mlr.press/v16/bodo11a.html %V 16 %X Active learning is an important field of machine learning and it is becoming more widely used in case of problems where labeling the examples in the training data set is expensive. In this paper we present a clustering-based algorithm used in the Active Learning Challenge (http://www.causality.inf.ethz.ch/activelearning.php). The algorithm is based on graph clustering with normalized cuts, and uses k-means to extract representative points from the data and approximate spectral clustering for efficiently performing the computations.
RIS
TY - CPAPER TI - Active Learning with Clustering AU - Zalán Bodó AU - Zsolt Minier AU - Lehel Csató BT - Active Learning and Experimental Design workshop In conjunction with AISTATS 2010 DA - 2011/04/21 ED - Isabelle Guyon ED - Gavin Cawley ED - Gideon Dror ED - Vincent Lemaire ED - Alexander Statnikov ID - pmlr-v16-bodo11a PB - PMLR DP - Proceedings of Machine Learning Research VL - 16 SP - 127 EP - 139 L1 - http://proceedings.mlr.press/v16/bodo11a/bodo11a.pdf UR - https://proceedings.mlr.press/v16/bodo11a.html AB - Active learning is an important field of machine learning and it is becoming more widely used in case of problems where labeling the examples in the training data set is expensive. In this paper we present a clustering-based algorithm used in the Active Learning Challenge (http://www.causality.inf.ethz.ch/activelearning.php). The algorithm is based on graph clustering with normalized cuts, and uses k-means to extract representative points from the data and approximate spectral clustering for efficiently performing the computations. ER -
APA
Bodó, Z., Minier, Z. & Csató, L.. (2011). Active Learning with Clustering. Active Learning and Experimental Design workshop In conjunction with AISTATS 2010, in Proceedings of Machine Learning Research 16:127-139 Available from https://proceedings.mlr.press/v16/bodo11a.html.

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