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. © Springer-Verlag Berlin Heidelberg 2003.
CITATION STYLE
Xing, Y., Madden, M. G., Duggan, J., & Lyons, G. J. (2003). Distributed regression for heterogeneous data sets. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2810, 544–553. https://doi.org/10.1007/978-3-540-45231-7_50
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