@inproceedings{a6b3dc2f71e948efa77b0bcb8b75c923,
title = "Fast implementation of neural network classification",
abstract = "Most artificial neural networks use a nonlinear activation function that includes sigmoid and hyperbolic tangent functions. Most artificial networks employ nonlinear functions such as these sigmoid and hyperbolic tangent functions, which incur high complexity costs, particularly during hardware implementation. In this paper, we propose new polynomial approximation methods for nonlinear activation functions that can substantially reduce complexity without sacrificing performance. The proposed approximation methods were applied to pattern classification problems. Experimental results show that the processing time was reduced by up to 50% without any performance degradations in terms of computer simulation.",
author = "Guiwon Seo and Jiheon Ok and Chulhee Lee",
year = "2013",
doi = "10.1117/12.2026666",
language = "English",
isbn = "9780819497215",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
booktitle = "Satellite Data Compression, Communications, and Processing IX",
address = "United States",
note = "Satellite Data Compression, Communications, and Processing IX ; Conference date: 26-08-2013 Through 27-08-2013",
}