A Survey on Transfer Learning Export

@article{citeulike:5941312, abstract = {A major assumption in many machine learning and data mining systems is that the data must be from the same feature representations and that the data distributions in the training and test data are the same. However, in many real-world applications, this assumption does not hold. For example, we sometimes have a classification task in one task domain, but we only have sufficient training data in another task domain where the data may be in a different feature space or follow a different distribution. In these cases, knowledge transfer, if done successfully, would greatly benefit learning in our interested domain by avoiding expensive data labeling tasks. In recent years, \emph{transfer learning} has emerged as a new technique to address this problem. This survey focuses on categorizing and reviewing the current progress on transfer learning for classification, regression and clustering problems. We discuss the relationship between transfer learning and other related research areas, such as domain adaptation, multi-task learning and sample selection bias as well as co-variate shift, and explore some potential future problems in knowledge transfer research.}, address = {Los Alamitos, CA, USA}, author = {Pan, Sinno J. and Yang, Qiang}, citeulike-article-id = {5941312}, citeulike-linkout-0 = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2009.191}, citeulike-linkout-1 = {http://dx.doi.org/10.1109/TKDE.2009.191}, doi = {10.1109/TKDE.2009.191}, issn = {1041-4347}, journal = {IEEE Transactions on Knowledge and Data Engineering}, keywords = {survey, transfer-learning}, number = {1}, posted-at = {2009-10-14 20:41:16}, priority = {2}, publisher = {IEEE Computer Society}, title = {A Survey on Transfer Learning}, url = {http://dx.doi.org/10.1109/TKDE.2009.191}, volume = {99}, year = {5555} }

TY - JOUR ID - citeulike:5941312 L3 - citeulike-article-id:5941312 N2 - A major assumption in many machine learning and data mining systems is that the data must be from the same feature representations and that the data distributions in the training and test data are the same. However, in many real-world applications, this assumption does not hold. For example, we sometimes have a classification task in one task domain, but we only have sufficient training data in another task domain where the data may be in a different feature space or follow a different distribution. In these cases, knowledge transfer, if done successfully, would greatly benefit learning in our interested domain by avoiding expensive data labeling tasks. In recent years, \emph{transfer learning} has emerged as a new technique to address this problem. This survey focuses on categorizing and reviewing the current progress on transfer learning for classification, regression and clustering problems. We discuss the relationship between transfer learning and other related research areas, such as domain adaptation, multi-task learning and sample selection bias as well as co-variate shift, and explore some potential future problems in knowledge transfer research. IS - 1 JF - IEEE Transactions on Knowledge and Data Engineering SN - 1041-4347 AD - Los Alamitos, CA, USA TI - A Survey on Transfer Learning PB - IEEE Computer Society VL - 99 KW - survey KW - transfer-learning AU - Pan, Sinno AU - Yang, Qiang PY - 5555/// UR - http://dx.doi.org/10.1109/TKDE.2009.191 ER -