Online kernel classification with adjustable bandwidth using control-based learning approach

作者：

Highlights：

• Proposing two novel adaptive classification algorithms with adjustable kernel size.

• A novel approach to deal with nonlinear learning problems.

• Optimal control can be an effective approach for developing adaptive learning algorithms.

摘要

•Proposing two novel adaptive classification algorithms with adjustable kernel size.•A novel approach to deal with nonlinear learning problems.•Optimal control can be an effective approach for developing adaptive learning algorithms.

论文关键词：Online classification,Kernel learning,Adaptive learning,Adjustable bandwidth,Control-based approach

论文评审过程：Received 22 June 2019, Revised 4 May 2020, Accepted 28 July 2020, Available online 29 July 2020, Version of Record 2 August 2020.

论文官网地址：https://doi.org/10.1016/j.patcog.2020.107566