Active Learning with Hinted Support Vector Machine

Chun-Liang Li, Chun-Sung Ferng, Hsuan-Tien Lin
Proceedings of the Asian Conference on Machine Learning, PMLR 25:221-235, 2012.

Abstract

The abundance of real-world data and limited labeling budget calls for active learning, which is an important learning paradigm for reducing human labeling efforts. Many recently developed active learning algorithms consider both uncertainty and representativeness when making querying decisions. However, exploiting representativeness with uncertainty concurrently usually requires tackling sophisticated and challenging learning tasks, such as clustering. In this paper, we propose a new active learning framework, called hinted sampling, which takes both uncertainty and representativeness into account in a simpler way. We design a novel active learning algorithm within the hinted sampling framework with an extended support vector machine. Experimental results validate that the novel active learning algorithm can result in a better and more stable performance than that achieved by state-of-the-art algorithms.

Cite this Paper

BibTeX
@InProceedings{pmlr-v25-li12, title = {Active Learning with Hinted Support Vector Machine}, author = {Li, Chun-Liang and Ferng, Chun-Sung and Lin, Hsuan-Tien}, booktitle = {Proceedings of the Asian Conference on Machine Learning}, pages = {221--235}, year = {2012}, editor = {Hoi, Steven C. H. and Buntine, Wray}, volume = {25}, series = {Proceedings of Machine Learning Research}, address = {Singapore Management University, Singapore}, month = {04--06 Nov}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v25/li12/li12.pdf}, url = {https://proceedings.mlr.press/v25/li12.html}, abstract = {The abundance of real-world data and limited labeling budget calls for active learning, which is an important learning paradigm for reducing human labeling efforts. Many recently developed active learning algorithms consider both uncertainty and representativeness when making querying decisions. However, exploiting representativeness with uncertainty concurrently usually requires tackling sophisticated and challenging learning tasks, such as clustering. In this paper, we propose a new active learning framework, called hinted sampling, which takes both uncertainty and representativeness into account in a simpler way. We design a novel active learning algorithm within the hinted sampling framework with an extended support vector machine. Experimental results validate that the novel active learning algorithm can result in a better and more stable performance than that achieved by state-of-the-art algorithms.} }
Endnote
%0 Conference Paper %T Active Learning with Hinted Support Vector Machine %A Chun-Liang Li %A Chun-Sung Ferng %A Hsuan-Tien Lin %B Proceedings of the Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2012 %E Steven C. H. Hoi %E Wray Buntine %F pmlr-v25-li12 %I PMLR %P 221--235 %U https://proceedings.mlr.press/v25/li12.html %V 25 %X The abundance of real-world data and limited labeling budget calls for active learning, which is an important learning paradigm for reducing human labeling efforts. Many recently developed active learning algorithms consider both uncertainty and representativeness when making querying decisions. However, exploiting representativeness with uncertainty concurrently usually requires tackling sophisticated and challenging learning tasks, such as clustering. In this paper, we propose a new active learning framework, called hinted sampling, which takes both uncertainty and representativeness into account in a simpler way. We design a novel active learning algorithm within the hinted sampling framework with an extended support vector machine. Experimental results validate that the novel active learning algorithm can result in a better and more stable performance than that achieved by state-of-the-art algorithms.
RIS
TY - CPAPER TI - Active Learning with Hinted Support Vector Machine AU - Chun-Liang Li AU - Chun-Sung Ferng AU - Hsuan-Tien Lin BT - Proceedings of the Asian Conference on Machine Learning DA - 2012/11/17 ED - Steven C. H. Hoi ED - Wray Buntine ID - pmlr-v25-li12 PB - PMLR DP - Proceedings of Machine Learning Research VL - 25 SP - 221 EP - 235 L1 - http://proceedings.mlr.press/v25/li12/li12.pdf UR - https://proceedings.mlr.press/v25/li12.html AB - The abundance of real-world data and limited labeling budget calls for active learning, which is an important learning paradigm for reducing human labeling efforts. Many recently developed active learning algorithms consider both uncertainty and representativeness when making querying decisions. However, exploiting representativeness with uncertainty concurrently usually requires tackling sophisticated and challenging learning tasks, such as clustering. In this paper, we propose a new active learning framework, called hinted sampling, which takes both uncertainty and representativeness into account in a simpler way. We design a novel active learning algorithm within the hinted sampling framework with an extended support vector machine. Experimental results validate that the novel active learning algorithm can result in a better and more stable performance than that achieved by state-of-the-art algorithms. ER -
APA
Li, C., Ferng, C. & Lin, H.. (2012). Active Learning with Hinted Support Vector Machine. Proceedings of the Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 25:221-235 Available from https://proceedings.mlr.press/v25/li12.html.

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