%0 Conference Paper %1 citeulike:105008 %A Joachims, Thorsten %B Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003) %D 2003 %K clustering contraint spectral %T Transductive Learning via Spectral Graph Partitioning %X 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.

@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.}, added-at = {2007-04-28T16:01:00.000+0200}, author = {Joachims, Thorsten}, biburl = {https://www.bibsonomy.org/bibtex/227daef0514080660b9e25e27a5d41b64/kzhou}, booktitle = {Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003)}, citeulike-article-id = {105008}, interhash = {1686cf7cd6cc2b7b0825148c4742b8da}, intrahash = {27daef0514080660b9e25e27a5d41b64}, keywords = {clustering contraint spectral}, priority = {4}, timestamp = {2007-04-28T16:01:00.000+0200}, title = {Transductive Learning via Spectral Graph Partitioning}, year = 2003 }