Transductive Learning via Spectral Graph Partitioning Export

@inproceedings{citeulike:105008, abstract = {We present a new method for transductive learning, which can be seen as a transductive version of the k nearest-neighbor classifier. Unlike for many other transductive learning methods, the training problem has a meaningful relaxation that can be solved globally optimally using spectral methods. We propose an algorithm that robustly achieves good generalization performance and that can be trained efficiently. A key advantage of the algorithm is that it does not require additional heuristics to avoid unbalanced splits. Furthermore, we show a connection to transductive Support Vector Machines, and that an effective Co-Training algorithm arises as a special case.}, address = {Washington DC}, author = {Joachims, Thorsten}, booktitle = {Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003)}, citeulike-article-id = {105008}, citeulike-linkout-0 = {http://www.cs.cornell.edu/People/tj/publications/joachims_03a.pdf}, keywords = {clustering, contraint, spectral}, posted-at = {2005-09-01 16:46:05}, priority = {4}, title = {Transductive Learning via Spectral Graph Partitioning}, url = {http://www.cs.cornell.edu/People/tj/publications/joachims_03a.pdf}, year = {2003} }

TY - CONF ID - citeulike:105008 L3 - citeulike-article-id:105008 N2 - We present a new method for transductive learning, which can be seen as a transductive version of the k nearest-neighbor classifier. Unlike for many other transductive learning methods, the training problem has a meaningful relaxation that can be solved globally optimally using spectral methods. We propose an algorithm that robustly achieves good generalization performance and that can be trained efficiently. A key advantage of the algorithm is that it does not require additional heuristics to avoid unbalanced splits. Furthermore, we show a connection to transductive Support Vector Machines, and that an effective Co-Training algorithm arises as a special case. AD - Washington DC TI - Transductive Learning via Spectral Graph Partitioning T2 - Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003) KW - clustering KW - contraint KW - spectral AU - Joachims, Thorsten PY - 2003/// UR - http://www.cs.cornell.edu/People/tj/publications/joachims_03a.pdf ER -