Distributed Regression for Heterogeneous Data Sets

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.

• Type: Conference
• Year: 2003
• Where: IDA
• Authors: Yan Xing, Michael G. Madden, Jim Duggan, Gerard Lyons
• Keywords: Context Heterogeneity, Data Mining Approaches, IDA 2003, Traditional Meta-learning Techniques