The Self-Organizing Restricted Boltzmann Machine for Deep Representation with the Application on Classification Problems

作者：

Highlights：

• The proposed approach can determine number of hidden layers in DBN and hidden neurons in RBM.

• It removes the need of trial and error for discovering the reasonable (or optimal) network structure.

• It is low cost from terms of time and computation.

• It is the self-organizing deep model.

• It acts as a regularization method and prevents over-fitting.

摘要

•The proposed approach can determine number of hidden layers in DBN and hidden neurons in RBM.•It removes the need of trial and error for discovering the reasonable (or optimal) network structure.•It is low cost from terms of time and computation.•It is the self-organizing deep model.•It acts as a regularization method and prevents over-fitting.

论文关键词：Deep learning,Self-organizing restricted Boltzmann machines,Separability-correlation measure,MNIST,Moore-set,Wisconsin breast cancer dataset

论文评审过程：Received 15 September 2018, Revised 26 November 2019, Accepted 5 February 2020, Available online 13 February 2020, Version of Record 19 February 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.113286