Success based locally weighted Multiple Kernel combination
作者：
Highlights：
• Base kernels have local Regions of Success (RoS) or expertise in feature space.
• RoS for each base kernel is identified during training from cross-validation set.
• These RoS are modeled using regression in terms of Success Prediction Functions (SPF).
• SPFs are used as instance dependent weighing functions of base kernels in MKL framework.
• Proposed weighing scheme maximizes alignment with Ideal Kernel.
摘要
•Base kernels have local Regions of Success (RoS) or expertise in feature space.•RoS for each base kernel is identified during training from cross-validation set.•These RoS are modeled using regression in terms of Success Prediction Functions (SPF).•SPFs are used as instance dependent weighing functions of base kernels in MKL framework.•Proposed weighing scheme maximizes alignment with Ideal Kernel.
论文关键词：Support vector machine,Multiple kernel learning,Kernel alignment,Localized multiple kernel learning,Regions of success,Success prediction functions,Feature selection,Kernel selection,Feature fusion,Support vector regression
论文评审过程：Received 10 June 2016, Revised 14 January 2017, Accepted 23 February 2017, Available online 28 February 2017, Version of Record 11 March 2017.
论文官网地址：https://doi.org/10.1016/j.patcog.2017.02.029
