期刊
NEUROCOMPUTING
卷 414, 期 -, 页码 172-181出版社
ELSEVIER
DOI: 10.1016/j.neucom.2020.07.015
关键词
Extreme Learning machines; Lanczos Algorithm; Regularization; Neural Networks; Regression; Classification
This paper presents a new regularization for Extreme Learning Machines (ELMs). ELMs are Randomized Neural Networks (RNNs) that are known for their fast training speed and good accuracy. Nevertheless the complexity of ELMs has to be selected, and regularization has to be performed in order to avoid under-fitting or overfitting. Therefore, a novel Regularization is proposed using a modified Lanczos Algorithm: Iterative Lanczos Extreme Learning Machine (Lan-ELM). As summarized in the experimental Section, the computational time is on average divided by 4 and the Normalized MSE is on average reduced by 11%. In addition, the proposed method can be intuitively parallelized, which makes it a very valuable tool to analyze huge data sets in real-time. (C) 2020 Elsevier B.V. All rights reserved.
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