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- W55930812 abstract "Spatial data are typically affected by positive autocorrelation. Formally, autocorrelation is the property of random variables taking values at pairs of locations a certain distance apart which are more similar than expected for randomly associated pairs of observations. This property is related to the smoothness assumption made in transduction, according to if two points in a high-density region are close, then the corresponding outputs should also be close. Transduction in spatial data mining also finds motivation in the prior availability of large sets of unlabelled data to be predicted which is typical of spatial data applications. We propose a spatial regression method, named SpReCo, that works in a transductive setting and employs a co-training technique to learn separate learners: one based on attribute values measured at some specific spatial positions, and the other based on attribute values derived by aggregating measurements in the neighborhood of those spatial positions. Each learner labels the unlabeled data for the other during the learning process. Predictive accuracy is evaluated on several spatial databases." @default.
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- W55930812 date "2009-01-01" @default.
- W55930812 modified "2023-09-28" @default.
- W55930812 title "Spatial Regression in the Transductive Setting." @default.
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