Online co-regularized algorithms
conference paper
We propose an online co-regularized learning algorithm for classification and regression tasks. We demonstrate that by sequentially co-regularizing prediction functions on unlabeled data points, our algorithm provides improved performance in comparison to supervised methods on several UCI benchmarks and a real world natural language processing dataset. The presented algorithm is particularly applicable to learning tasks where large amounts of (unlabeled) data are available for training. We also provide an easy to set-up and use Python implementation of our algorithm. © 2012 Springer-Verlag Berlin Heidelberg.
TNO Identifier
465812
ISSN
03029743
ISBN
9783642334917
Source title
15th International Conference on Discovery Science, DS 2012, 29 October 2012 through 31 October 2012, Lyon
Pages
184-193
Files
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