KeCo: Kernel-based online co-agreement algorithm

KeCo: Kernel-based online co-agreement algorithm

Wiel, L.
Heskes, T.
Levin, E.

Japkowicz, N. (editor)
Matwin, S. (editor)

2015

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).

Life
MSB - Microbiology and Systems Biology
ELSS - Earth, Life and Social Sciences
Biomedical Innovation
Biology
Healthy Living
Classification
Co-agreement
Kernel
Large-scale
Multi-view
Non-linear
Online
Semi-supervised

http://resolver.tudelft.nl/uuid:b73f7189-766d-4940-b04a-d9866c4cc773

https://doi.org/10.1007/978-3-319-24282-8

529434

Springer International Publishing

9783319242811

18th International Conference on Discovery Science, DS 2015; Banff; Canada; 4 October 2015 through 6 October 2015, 9356, 308-315

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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