Risk Bounds for Random Regression Graphs

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Risk Bounds for Random Regression Graphs
We consider the regression problem and describe an algorithm approximating the regression function by estimators piecewise constant on the elements of an adaptive partition. The partitions are iteratively constructed by suitable random merges and splits, using cuts of arbitrary geometry. We give a risk bound under the assumption that a “weak learning hypothesis” holds, and characterize this hypothesis in terms of a suitable RKHS.
Andrea Caponnetto, Steve Smale
Added 14 Dec 2010
Updated 14 Dec 2010
Type Journal
Year 2007
Where FOCM
Authors Andrea Caponnetto, Steve Smale
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