@inproceedings{90a619eae7f140ea88fe5562214f1b03,
title = "Improving the performance of extreme learning machine for hyperspectral image classification",
abstract = "Extreme learning machine (ELM) and kernel ELM (KELM) can offer comparable performance as the standard powerful classifier{\^a}ۥsupport vector machine (SVM), but with much lower computational cost due to extremely simple training step. However, their performance may be sensitive to several parameters, such as the number of hidden neurons. An empirical linear relationship between the number of training samples and the number of hidden neurons is proposed. Such a relationship can be easily estimated with two small training sets and extended to large training sets so as to greatly reduce computational cost. Other parameters, such as the steepness parameter in the sigmodal activation function and regularization parameter in the KELM, are also investigated. The experimental results show that classification performance is sensitive to these parameters; fortunately, simple selections will result in suboptimal performance.",
keywords = "Classification, Extreme Learning Machine, Hyperspectral Imagery, Kernel Method, Neural Network, Support Vector Machine",
author = "Jiaojiao Li and Qian Du and Wei Li and Yunsong Li",
note = "Publisher Copyright: {\textcopyright} 2015 SPIE.; Satellite Data Compression, Communications, and Processing XI ; Conference date: 23-04-2015 Through 24-04-2015",
year = "2015",
doi = "10.1117/12.2178013",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Yunsong Li and Chein-I Chang and Bormin Huang and Qian Du and Chulhee Lee",
booktitle = "Satellite Data Compression, Communications, and Processing XI",
address = "United States",
}