Adaptive Second-order Derivative Approximate Greatest Descent Optimization for Deep Learning Neural Networks

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Adaptive Second-order Derivative Approximate Greatest Descent Optimization for Deep Learning Neural Networks

Backpropagation using Stochastic Diagonal Approximate Greatest Descent (SDAGD) is a novel adaptive second-order derivative optimization method in updating weights of deep learning neural networks. SDAGD applies two-phase switching strategy to seek for solution at far using long-term optimal trajecto...

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Bibliographic Details
Main Author:	Tan, Hong Hui
Format:	Thesis
Published:	Curtin University 2019
Online Access:	http://hdl.handle.net/20.500.11937/77991

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