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- W2556554004 abstract "When dealing with subjective, noisy, or otherwise nebulous features, the wisdom of crowds suggests that one may benefit from multiple judgments of the same feature on the same object. We give theoretically-motivated `feature multi-selection' algorithms that choose, among a large set of candidate features, not only which features to judge but how many times to judge each one. We demonstrate the effectiveness of this approach for linear regression on a crowdsourced learning task of predicting people's height and weight from photos, using features such as 'gender' and 'estimated weight' as well as culturally fraught ones such as 'attractive'." @default.
- W2556554004 created "2016-11-30" @default.
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- W2556554004 date "2016-11-24" @default.
- W2556554004 modified "2023-09-26" @default.
- W2556554004 title "Feature Multi-Selection among Subjective Features" @default.
- W2556554004 hasPublicationYear "2016" @default.
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