H. Kargupta, B. Park, E. Johnson, E. Sanseverino, L. Silvestre, and D. Hershberger. Collective Data Mining From Distributed Vertically Partitioned Feature Space. In Workshop on distributed data mining. International ConferenceonKnowledge Discovery and Data Mining., 1998. Home/Search Document Details and Download
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A Framework for Finding Distributed Data Mining Strategies.. - Turinsky, Grossman (2000) (7 citations) (Correct)
....[10] stacking [12] mixture of experts [13] etc. Several systems for analysis of distributed data havebeen developed in recentyears. These include the JAM system developed by Stolfo et al. [11] the Kensington system developed by Guo et al. [7] and BODHI developed by Kargupta et al. [8] [9]. These systems di er in several ways. For example, JAM uses meta learning that combines several models by building a separate meta model whose inputs are the outputs of the collection of models and whose output is the desired outcome. Kensington employs knowledge probing that considers learning ....
H. Kargupta, B. Park, E. Johnson, E. Sanseverino, L. D. Silvestre, and D. Hershberger. Collectivedata mining from distributed vertically partitioned feature space. Workshop on distributed data mining, International ConferenceonKnowledge Discovery and Data Mining, 1998.
Distributed Data Mining Bibliography - Hillol Self-citation (Kargupta) (Correct)
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H. Kargupta, B. Park, E. Johnson, E. Sanseverino, L. Silvestre, and D. Hershberger. Collective Data Mining From Distributed Vertically Partitioned Feature Space. In Workshop on distributed data mining. International ConferenceonKnowledge Discovery and Data Mining., 1998.
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