Intelligent Information Management

Volume 10, Issue 4 (July 2018)

ISSN Print: 2160-5912   ISSN Online: 2160-5920

Google-based Impact Factor: 1.70  Citations  h5-index & Ranking

An Optimization of Neural Network Hyper-Parameter to Increase Its Performance

HTML  XML Download Download as PDF (Size: 329KB)  PP. 99-107  
DOI: 10.4236/iim.2018.104008    591 Downloads   1,274 Views   Citations
Author(s)

ABSTRACT

With the boost of artificial intelligence, the study of neural network intrigues scientists. Artificial neural network, which was first designed theoretically in 1943 based on understanding of human brains, demonstrated impressing computational and learning capabilities. In this paper, we investigated the neural network’s learning capability by using a feed-forward neural network to recognize human’s digit hand-writing. Controlled experiments were executed by changing the input values of different parameters, such as learning rates and hidden layer units. After investigating upon the effects of each parameter on the overall learning performance of the neural network, we concluded that, when an intermediate value of one given parameter was implemented, the neural network achieved the highest learning efficiency, and potential problems like over-fitting would be prevented.

Cite this paper

Fu, Y. (2018) An Optimization of Neural Network Hyper-Parameter to Increase Its Performance. Intelligent Information Management, 10, 99-107. doi: 10.4236/iim.2018.104008.

Copyright © 2020 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.