Discriminative learning for differing training and test distributions Export

@inproceedings{citeulike:1487431, abstract = {We address classification problems for which the training instances are governed by a distribution that is allowed to differ arbitrarily from the test distribution---problems also referred to as classification under covariate shift. We derive a solution that is purely discriminative: neither training nor test distribution are modeled explicitly. We formulate the general problem of learning under covariate shift as an integrated optimization problem. We derive a kernel logistic regression classifier for differing training and test distributions.}, address = {New York, NY, USA}, author = {Bickel, Steffen and Br\"{u}ckner, Michael and Scheffer, Tobias}, booktitle = {ICML '07: Proceedings of the 24th international conference on Machine learning}, citeulike-article-id = {1487431}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1273496.1273507}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1273496.1273507}, doi = {10.1145/1273496.1273507}, isbn = {978-1-59593-793-3}, keywords = {bias, dataset, learning, selection, shift, transfer}, location = {Corvalis, Oregon}, pages = {81--88}, posted-at = {2009-06-03 10:57:21}, priority = {2}, publisher = {ACM}, title = {Discriminative learning for differing training and test distributions}, url = {http://dx.doi.org/10.1145/1273496.1273507}, year = {2007} }

TY - CONF ID - citeulike:1487431 L3 - citeulike-article-id:1487431 N2 - We address classification problems for which the training instances are governed by a distribution that is allowed to differ arbitrarily from the test distribution---problems also referred to as classification under covariate shift. We derive a solution that is purely discriminative: neither training nor test distribution are modeled explicitly. We formulate the general problem of learning under covariate shift as an integrated optimization problem. We derive a kernel logistic regression classifier for differing training and test distributions. CY - Corvalis, Oregon SN - 978-1-59593-793-3 AD - New York, NY, USA EP - 88 TI - Discriminative learning for differing training and test distributions PB - ACM T2 - ICML '07: Proceedings of the 24th international conference on Machine learning SP - 81 KW - bias KW - dataset KW - learning KW - selection KW - shift KW - transfer AU - Bickel, Steffen AU - Brückner, Michael AU - Scheffer, Tobias PY - 2007/// UR - http://dx.doi.org/10.1145/1273496.1273507 ER -