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- W2153711228 abstract "We explore the possibility of importing black-box models learned over data sources at remote sites to improve models learned over locally available 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 performance when data overlap among different sites." @default.
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- W2153711228 title "Sharing learned models among remote database partitions by local meta-learning" @default.
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