Abstract
Existing meta-learning based distributed data mining approaches do not explicitly address context heterogeneity across individual sites. This limitation constrains their applications where distributed data are not identically and independently distributed. Modeling heterogeneously distributed data with hierarchical models, this paper extends the traditional meta-learning techniques so that they can be successfully used in distributed scenarios with context heterogeneity.
The support of the Informatics Research Initiative of Enterprise Ireland is gratefully acknowledged.
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Xing, Y., Madden, M.G., Duggan, J., Lyons, G.J. (2003). Distributed Regression for Heterogeneous Data Sets. In: R. Berthold, M., Lenz, HJ., Bradley, E., Kruse, R., Borgelt, C. (eds) Advances in Intelligent Data Analysis V. IDA 2003. Lecture Notes in Computer Science, vol 2810. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45231-7_50
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DOI: https://doi.org/10.1007/978-3-540-45231-7_50
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