Learning from data of variable quality Export

@inproceedings{citeulike:4220845, abstract = {We initiate the study of learning from multiple sources of limited data, each of which may be corrupted at a different rate. We develop a complete theory of which data sources should be used for two fundamental problems: estimating the bias of a coin, and learning a classifier in the presence of label noise. In both cases, efficient algorithms are provided for computing the optimal subset of data. 1}, author = {Crammer, Koby and Kearns, Michael and Wortman, Jennifer}, booktitle = {In NIPS 18}, citeulike-article-id = {4220845}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.61.5430}, keywords = {class\_probability\_estimation, classification, data, noise, theory}, posted-at = {2009-03-26 01:23:42}, priority = {3}, title = {Learning from data of variable quality}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.61.5430}, volume = {2006}, year = {1995} }

TY - CONF ID - citeulike:4220845 L3 - citeulike-article-id:4220845 N2 - We initiate the study of learning from multiple sources of limited data, each of which may be corrupted at a different rate. We develop a complete theory of which data sources should be used for two fundamental problems: estimating the bias of a coin, and learning a classifier in the presence of label noise. In both cases, efficient algorithms are provided for computing the optimal subset of data. 1 TI - Learning from data of variable quality VL - 2006 T2 - In NIPS 18 KW - class_probability_estimation KW - classification KW - data KW - noise KW - theory AU - Crammer, Koby AU - Kearns, Michael AU - Wortman, Jennifer PY - 1995/// UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.61.5430 ER -