Analyzing co-training style algorithms (2007)
| Venue: | in Proceedings of the 18th European Conference on Machine Learning |
| Citations: | 6 - 4 self |
BibTeX
@INPROCEEDINGS{Wang07analyzingco-training,
author = {Wei Wang and Zhi-hua Zhou},
title = {Analyzing co-training style algorithms},
booktitle = {in Proceedings of the 18th European Conference on Machine Learning},
year = {2007}
}
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Abstract
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. 1
