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Reliable nearest neighbors for lazy learning | IEEE Conference Publication | IEEE Xplore

Reliable nearest neighbors for lazy learning


Abstract:

A key problem of memory based learning methods is the selection of a good smoothing or bandwidth parameter that defines the region over which generalization is performed....Show More

Abstract:

A key problem of memory based learning methods is the selection of a good smoothing or bandwidth parameter that defines the region over which generalization is performed. In this article we present a novel algorithm to answer this question by utilizing the information from confidence intervals, to compute a bandwidth. The basic idea is the usage of confidence intervals to get a statistical statement about the quality of fit between estimated model and process. As long as the prediction intervals of a certain model include the neighboring data points of an incremental growing validity region, it is considered to be a good fit.
Date of Conference: 29 June 2011 - 01 July 2011
Date Added to IEEE Xplore: 18 August 2011
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ISSN Information:

Conference Location: San Francisco, CA, USA

References

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