Nonlinear regression via incremental decision trees
作者：
Highlights：
• An online nonlinear regression algorithm is proposed.
• Optimal regressor space partition is inferred to manage convergence and undertraining.
• Performance of the optimal twice differentiable regression function is achieved.
• Performance guarantees hold for any data sequence without any statistical assumptions.
• Proposed algorithm is superior over the state-of-the-art techniques.
摘要
•An online nonlinear regression algorithm is proposed.•Optimal regressor space partition is inferred to manage convergence and undertraining.•Performance of the optimal twice differentiable regression function is achieved.•Performance guarantees hold for any data sequence without any statistical assumptions.•Proposed algorithm is superior over the state-of-the-art techniques.
论文关键词：Online regression,Sequential learning,Nonlinear models,Incremental decision trees
论文评审过程：Received 4 December 2017, Revised 7 July 2018, Accepted 27 August 2018, Available online 28 August 2018, Version of Record 7 September 2018.
论文官网地址：https://doi.org/10.1016/j.patcog.2018.08.014
