A framework for parameter estimation and model selection in kernel deep stacking networks
作者：
Highlights：
• Kernel deep stacking networks (KDSNs) are a novel method in biomedical research.
• KDSNs belong to the class of supervised deep learning.
• They are computationally faster to train than artificial neural networks.
• KDSNs require the specification of a large number of tuning parameters.
• We propose a new data-driven framework for model selection in KDSNs.
• The proposed methodology includes model-based optimization and hill climbing.
• No pre-specification of any of the KDSN tuning parameters is required.
• Application of the proposed methodology results in a fast tuning procedure.
• KDSNs are competitive with other techniques in the field of deep learning.
摘要
Highlights•Kernel deep stacking networks (KDSNs) are a novel method in biomedical research.•KDSNs belong to the class of supervised deep learning.•They are computationally faster to train than artificial neural networks.•KDSNs require the specification of a large number of tuning parameters.•We propose a new data-driven framework for model selection in KDSNs.•The proposed methodology includes model-based optimization and hill climbing.•No pre-specification of any of the KDSN tuning parameters is required.•Application of the proposed methodology results in a fast tuning procedure.•KDSNs are competitive with other techniques in the field of deep learning.
论文关键词：Deep learning,Artificial neural networks,Kernel regression,Model-based optimization
论文评审过程：Received 9 November 2015, Revised 9 March 2016, Accepted 21 April 2016, Available online 30 May 2016, Version of Record 6 June 2016.
论文官网地址：https://doi.org/10.1016/j.artmed.2016.04.002
