KeCo: Kernel-based online co-agreement algorithm
bookPart
We propose a kernel-based online semi-supervised algorithm that is applicable for large scale learning tasks. In particular, we use a multi-view learning framework and a co-agreement strategy to take into account unlabelled data and to improve classification performance of the algorithm. Unlike the standard online methods our algorithm is naturally applicable to many real-world situations where data is available in multiple representations. In addition our online algorithm allows learning non-linear relations in the data via kernel functions, that are efficiently embedded into the formulation of the algorithm. We test performance of the algorithm on several large-scale LIBSVM and UCI benchmark datasets and demonstrate improved performance in comparison to standard online learning methods. Last but not least, we make a Python implementation of our algorithm available for download (Available at https://github.com/laurensvdwiel/KeCo).
TNO Identifier
529434
ISBN
978-3-319-24281-1
Publisher
Springer International Publishing
Source title
18th International Conference on Discovery Science, DS 2015; Banff; Canada; 4 October 2015 through 6 October 2015
Editor(s)
Japkowicz, N.
Matwin, S.
Matwin, S.
Pages
308-315
Files
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