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...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Text |
| Language: | English |
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| Online Access: | 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 |
| Summary: | 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 |
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