Pool-based active learning in approximate linear regression
نویسندگان
چکیده
منابع مشابه
Improving importance estimation in pool-based batch active learning for approximate linear regression
Pool-based batch active learning is aimed at choosing training inputs from a 'pool' of test inputs so that the generalization error is minimized. P-ALICE (Pool-based Active Learning using Importance-weighted least-squares learning based on Conditional Expectation of the generalization error) is a state-of-the-art method that can cope with model misspecification by weighting training samples acc...
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ژورنال
عنوان ژورنال: Machine Learning
سال: 2009
ISSN: 0885-6125,1573-0565
DOI: 10.1007/s10994-009-5100-3