Y. Guo, S. M. Rueger, J. Sutiwaraphun, J.; and J. Forbes-Millott, MetaLearnig for Parallel Data Mining, in Proceedings of the Seventh Parallel Computing Workshop, pages 1-2, 1997.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....at each site, and then moved to a common site where they are combined. Ensemble learning [3] is often used as a means of combining models built at geographically distributed sites. Methods for combining models in an ensemble include voting schemas [3] meta learning [11] knowledge probing [7], Bayesian model averaging and model selection [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 ....

....[7] Bayesian model averaging and model selection [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 ....

Y. Guo, S. M. Rueger, J. Sutiwaraphun, and J. Forbes-Millott. Meta-learnig for parallel data mining. Proceedings of the Seventh Parallel Computing Workshop, pages 1-2, 1997.

....of in this area is limited to data mining over commodity networks. This section is based in part on [7] Several systems have been developed for distributed data mining. Perhaps the most mature are: the JAM system developed by Stolfo et al. 16] the Kensington system developed by Guo et al. [9], and BODHI developed by Kargupta et al. 10] These systems differ in several ways: Data strategy. Distributed data mining can choose to move data, to move intermediate results, to move predictive models, or to move the final results of a data mining algorithm. Distributed data mining systems ....

.... meta model whose inputs are the outputs of the various models and whose output is the desired outcome [16] Knowledge probing considers learning from a black box viewpoint and creates an overall model by examining the input and the outputs to the various models, as well as the desired output [9]. Multiple models, or what are often called ensembles or committees of models, have been used for quite a while in (centralized) data mining. A variety of methods have been studied for combining models in an ensemble, including Bayesian model averaging and model selection [15] stacking [20] ....

Y. Guo, S. M. Rueger, J. Sutiwaraphun, J.; and J. Forbes-Millott, Meta-Learnig for Parallel Data Mining, in Proceedings of the Seventh Parallel Computing Workshop, pages 1-2, 1997.

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Y. Guo, S. M. Rueger, J. Sutiwaraphun, J.; and J. Forbes-Millott, MetaLearnig for Parallel Data Mining, in Proceedings of the Seventh Parallel Computing Workshop, pages 1-2, 1997.

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Y. Guo, S. M. Rueger, J. Sutiwaraphun, J.; and J. Forbes-Millott, MetaLearnig for Parallel Data Mining, in Proceedings of the Seventh Parallel Computing Workshop, pages 1-2, 1997.

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Y. Guo, S. M. Rueger, J. Sutiwaraphun, and J. Forbes-Millott, MetaLearnig for Parallel Data Mining, in Proceedings of the Seventh Parallel Computing Workshop, pages 1-2, 1997.

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