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.
CITATION STYLE
De Ruijter, T., Tsivtsivadze, E., & Heskes, T. (2012). Online co-regularized algorithms. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7569 LNAI, pp. 184–193). https://doi.org/10.1007/978-3-642-33492-4_16
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