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 crowd-sourced 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.
|Title of host publication||Proceedings of the 30th International Conference on Machine Learning (ICML), JMLR Workshop and Conference Proceedings|
|State||Published - 2013|
|Event||30th International Conference on Machine Learning, ICML 2013 - Atlanta, GA, United States|
Duration: 16 Jun 2013 → 21 Jun 2013
|Conference||30th International Conference on Machine Learning, ICML 2013|
|Period||16/06/13 → 21/06/13|