AL2: Learning for Active Learning
We introduce AL2, a pool-based active learning approach that learns how to in-form the active set selection. The framework is classifier-independent, amenable to different performance targets, applicable to both binary and multinomial classi-fication for batch-mode active learning. Here, we consider...
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ftciteseerx:oai:CiteSeerX.psu:10.1.1.650.7636 2023-05-15T14:55:04+02:00 AL2: Learning for Active Learning Bistra Dilkina Theodoros Damoulas Carla P. Gomes Daniel Fink The Pennsylvania State University CiteSeerX Archives application/pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.650.7636 http://www.cs.cornell.edu/~damoulas/Site/papers_files/activelearning-nipsworkshop.pdf en eng http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.650.7636 http://www.cs.cornell.edu/~damoulas/Site/papers_files/activelearning-nipsworkshop.pdf Metadata may be used without restrictions as long as the oai identifier remains attached to it. http://www.cs.cornell.edu/~damoulas/Site/papers_files/activelearning-nipsworkshop.pdf text ftciteseerx 2016-01-08T16:23:08Z We introduce AL2, a pool-based active learning approach that learns how to in-form the active set selection. The framework is classifier-independent, amenable to different performance targets, applicable to both binary and multinomial classi-fication for batch-mode active learning. Here, we consider a special instantiation, AL2submodular, in which the choice of learning structure leads to a submodular ob-jective function, therefore allowing for an efficient algorithm with optimality guar-antee of 1−1/e. Statistically significant improvements over the state of the art are offered for two supervised learning methods, benchmark (UCI) datasets and the motivating sustainability application of land-cover prediction in the Arctic. 1 Motivation and Related Work Sustainability research is inherently a predictive science and can be crucially informed by accu-rate models for e.g. species distributions, land-use and climate change [6, 8]. Consider a predictive model for land-cover in the Arctic that relates ecological covariates to vegetation type. Such a model enables projections of the possible effects of climate scenarios by predicting the future composition Text Arctic Climate change Unknown Arctic |
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We introduce AL2, a pool-based active learning approach that learns how to in-form the active set selection. The framework is classifier-independent, amenable to different performance targets, applicable to both binary and multinomial classi-fication for batch-mode active learning. Here, we consider a special instantiation, AL2submodular, in which the choice of learning structure leads to a submodular ob-jective function, therefore allowing for an efficient algorithm with optimality guar-antee of 1−1/e. Statistically significant improvements over the state of the art are offered for two supervised learning methods, benchmark (UCI) datasets and the motivating sustainability application of land-cover prediction in the Arctic. 1 Motivation and Related Work Sustainability research is inherently a predictive science and can be crucially informed by accu-rate models for e.g. species distributions, land-use and climate change [6, 8]. Consider a predictive model for land-cover in the Arctic that relates ecological covariates to vegetation type. Such a model enables projections of the possible effects of climate scenarios by predicting the future composition |
author2 |
The Pennsylvania State University CiteSeerX Archives |
format |
Text |
author |
Bistra Dilkina Theodoros Damoulas Carla P. Gomes Daniel Fink |
spellingShingle |
Bistra Dilkina Theodoros Damoulas Carla P. Gomes Daniel Fink AL2: Learning for Active Learning |
author_facet |
Bistra Dilkina Theodoros Damoulas Carla P. Gomes Daniel Fink |
author_sort |
Bistra Dilkina |
title |
AL2: Learning for Active Learning |
title_short |
AL2: Learning for Active Learning |
title_full |
AL2: Learning for Active Learning |
title_fullStr |
AL2: Learning for Active Learning |
title_full_unstemmed |
AL2: Learning for Active Learning |
title_sort |
al2: learning for active learning |
url |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.650.7636 http://www.cs.cornell.edu/~damoulas/Site/papers_files/activelearning-nipsworkshop.pdf |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Climate change |
genre_facet |
Arctic Climate change |
op_source |
http://www.cs.cornell.edu/~damoulas/Site/papers_files/activelearning-nipsworkshop.pdf |
op_relation |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.650.7636 http://www.cs.cornell.edu/~damoulas/Site/papers_files/activelearning-nipsworkshop.pdf |
op_rights |
Metadata may be used without restrictions as long as the oai identifier remains attached to it. |
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1766326864065855488 |