Analyzing co-training style algorithms

@inproceedings{WangZ07, abstract = {Co-training is a semi-supervised learning paradigm which trains two learners respectively from two different views and lets the learners label some unlabeled examples for each other. In this paper, we present a new PAC analysis on co-training style algorithms. We show that the co-training process can succeed even without two views, given that the two learners have large difference, which explains the success of some co-training style algorithms that do not require two views. Moreover, we theoretically explain that why the co-training process could not improve the performance further after a number of rounds, and present a rough estimation on the appropriate round to terminate co-training to avoid some wasteful learning rounds.}, address = {Warsaw, Poland}, author = {Wang, W. and Zhou, Z. H. }, booktitle = {Proceedings of the 18th European Conference on Machine Learning}, citeulike-article-id = {2966755}, doi = {10.1007/978-3-540-74958-5\_42}, journal = {Machine Learning: ECML 2007}, keywords = {co-training}, location = {Warsaw, Poland}, pages = {454--465}, posted-at = {2008-07-06 02:32:59}, priority = {2}, title = {Analyzing co-training style algorithms}, url = {http://dx.doi.org/10.1007/978-3-540-74958-5\_42}, year = {2007} }

TY - CONF ID - WangZ07 L3 - citeulike-article-id:2966755 TI - Analyzing co-training style algorithms T2 - Proceedings of the 18th European Conference on Machine Learning JF - Machine Learning: ECML 2007 SP - 454 EP - 465 AD - Warsaw, Poland CY - Warsaw, Poland N2 - Co-training is a semi-supervised learning paradigm which trains two learners respectively from two different views and lets the learners label some unlabeled examples for each other. In this paper, we present a new PAC analysis on co-training style algorithms. We show that the co-training process can succeed even without two views, given that the two learners have large difference, which explains the success of some co-training style algorithms that do not require two views. Moreover, we theoretically explain that why the co-training process could not improve the performance further after a number of rounds, and present a rough estimation on the appropriate round to terminate co-training to avoid some wasteful learning rounds. KW - co-training AU - Wang, W AU - Zhou, ZH PY - 2007/// UR - http://dx.doi.org/10.1007/978-3-540-74958-5_42 ER -