NDLI: An analysis based on F-discrepancy for sampling in regression tree learning

Content Provider	IEEE Xplore Digital Library
Author	Cervellera, C. Gaggero, M. Maccio, D.
Copyright Year	2014
Description	Author affiliation: Nat. Res. Council, Inst. of Intell. Syst. for Autom., Genoa, Italy (Cervellera, C.; Gaggero, M.; Maccio, D.)
Abstract	When the problem of learning from data is solved through a regression tree estimator, the quality of the available observations is an important issue, since it influences directly the accuracy of the resulting model. It becomes particuarly relevant when there is freedom to sample the input space arbitrarily to build the tree model or, alternatively, when we need to select a subsample to train the tree estimator on a computationally feasible input set, or to evaluate the goodness of the estimation on a test set. Here the accuracy of estimation based on regression trees is analyzed from the point of view of geometric properties of the available input data. In particular, the concept of F-discrepancy, a quantity that measures how well a set of points represents the distribution underlying the input generation process, is applied to derive conditions for convergence to the optimal piecewise-constant estimator for the unknown function we want to learn. The analysis has a constructive nature, allowing to select in practice good input sets for the problem at hand, as shown in a simulation example involving a real data set.
Starting Page	1115
Ending Page	1121
File Size	634473
Page Count	7
File Format	PDF
ISSN	21614407
e-ISBN	9781479914845
DOI	10.1109/IJCNN.2014.6889665
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Regression tree analysis Vegetation Accuracy Convergence Computational modeling Face Estimation
Content Type	Text
Resource Type	Article

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