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Nonparametric active learning for cost-sensitive classification
Cost-sensitive learning is a common type of machine learning problem where different errors of prediction incur different costs.
Nonparametric adaptive active learning under local smoothness condition
Active learning is typically used to label data, when the labeling process is expensive.
K-NN active learning under local smoothness assumption
Additionally, our algorithm avoids the strong density assumption that supposes the existence of the density function of the marginal distribution of the instance space and is therefore more generally applicable.
K-nn active learning under local smoothness condition
There is a large body of work on convergence rates either in passive or active learning.
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