Active Semi-Supervised Learning = Robust Multi-View Learning Export

@inproceedings{citeulike:2282780, abstract = {In a multi-view problem, the features of the domain can be partitioned into disjoint subsets (views) that are sufficient to learn the target concept.}, author = {Muslea, Ion and Minton, Steven and Knoblock, Craig A.}, booktitle = {Proceedings of 17th International Conference in Machine Learning}, citeulike-article-id = {2282780}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.18.4294}, keywords = {active, active\_learning, learning, semi-supervised}, pages = {435--442}, posted-at = {2008-01-24 02:20:08}, priority = {2}, title = {Active Semi-Supervised Learning = Robust Multi-View Learning}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.18.4294}, year = {2002} }

TY - CONF ID - citeulike:2282780 L3 - citeulike-article-id:2282780 N2 - In a multi-view problem, the features of the domain can be partitioned into disjoint subsets (views) that are sufficient to learn the target concept. EP - 442 TI - Active Semi-Supervised Learning = Robust Multi-View Learning T2 - Proceedings of 17th International Conference in Machine Learning SP - 435 KW - active KW - active_learning KW - learning KW - semi-supervised AU - Muslea, Ion AU - Minton, Steven AU - Knoblock, Craig PY - 2002/// UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.18.4294 ER -