Sharing Learned Models among Remote Database Partitions by Local Meta-Learning

Philip K. Chan, Salvatore J. Stolfo

We explore the possibility of importing "black box" models learned over data sources at remote sites to improve models learned over locally avail able data sources. In this way, we may be able to learn more accurate knowledge from globally available data than would otherwise be possible from partial, locally available data. Proposed meta-learning strategies in our previous work are extended to integrate local and remote models. We also investigate the effect on accuracy perfor mance when data overlap among different sites.

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