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• W2034602769 abstract "In this paper, we investigate adaptive nonlinear regression and introduce tree based piecewise linear regression algorithms that are highly efficient and provide significantly improved performance with guaranteed upper bounds in an individual sequence manner. We use a tree notion in order to partition the space of regressors in a nested structure. The introduced algorithms adapt not only their regression functions but also the complete tree structure while achieving the performance of the best linear mixture of a doubly exponential number of partitions, with a computational complexity only polynomial in the number of nodes of the tree. While constructing these algorithms, we also avoid using any artificial weighting of models (with highly data dependent parameters) and, instead, directly minimize the final regression error, which is the ultimate performance goal. The introduced methods are generic such that they can readily incorporate different tree construction methods such as random trees in their framework and can use different regressor or partitioning functions as demonstrated in the paper." @default.
• W2034602769 created "2016-06-24" @default.
• W2034602769 creator A5061392794 @default.
• W2034602769 creator A5089040739 @default.
• W2034602769 date "2013-11-25" @default.
• W2034602769 modified "2023-09-27" @default.
• W2034602769 title "A Comprehensive Approach to Universal Piecewise Nonlinear Regression Based on Trees" @default.
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• W2034602769 hasPublicationYear "2013" @default.
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