Adaptive multiple optimal learning factors for neural network training
This work addresses a specific optimization problem in neural network training, offering incremental improvements for researchers and practitioners in machine learning.
The paper tackled the challenge of determining optimal learning factors in neural network training by proposing the AMOLF algorithm, which dynamically adjusts learning factors based on error change, resulting in improved training efficiency and accuracy as demonstrated in experiments against methods like OWO-MOLF and Levenberg-Marquardt.
This thesis presents a novel approach to neural network training that addresses the challenge of determining the optimal number of learning factors. The proposed Adaptive Multiple Optimal Learning Factors (AMOLF) algorithm dynamically adjusts the number of learning factors based on the error change per multiply, leading to improved training efficiency and accuracy. The thesis also introduces techniques for grouping weights based on the curvature of the objective function and for compressing large Hessian matrices. Experimental results demonstrate the superior performance of AMOLF compared to existing methods like OWO-MOLF and Levenberg-Marquardt.