Classification using hierarchical mixture of discriminative learners: How to achieve high scores with few resources?
作者：
Highlights：
• We design a generalized framework for hierarchical mixture of experts.
• Several forms of weighting functions can be combined in the same architecture.
• A suitable choice of the weight functions will reduce the number of experts used.
• This reduction is shown through two examples derived from the generalized framework.
摘要
•We design a generalized framework for hierarchical mixture of experts.•Several forms of weighting functions can be combined in the same architecture.•A suitable choice of the weight functions will reduce the number of experts used.•This reduction is shown through two examples derived from the generalized framework.
论文关键词：Discriminative learner,Hierarchical mixture of experts,Input-dependent weight,Model selection
论文评审过程：Received 31 July 2017, Revised 16 November 2017, Accepted 22 November 2017, Available online 23 November 2017, Version of Record 22 December 2017.
论文官网地址：https://doi.org/10.1016/j.eswa.2017.11.046
