From Event: SPIE Optical Engineering + Applications, 2017
In neural networks, the training/predicting accuracy and algorithm efficiency can be improved significantly via accurate input feature extraction. In this study, some spatial features of several important factors in retrieving surface ultraviolet (UV) are extracted. An extreme learning machine (ELM) is used to retrieve the surface UV of 2014 in the continental United States, using the extracted features. The results conclude that more input weights can improve the learning capacities of neural networks.
Zhibin Sun, Ni-Bin Chang, Wei Gao, Maosi Chen, and Melina Zempila, "Using input feature information to improve ultraviolet retrieval in neural networks," Proc. SPIE 10405, Remote Sensing and Modeling of Ecosystems for Sustainability XIV, 1040506 (Presented at SPIE Optical Engineering + Applications: August 09, 2017; Published: 1 September 2017); https://doi.org/10.1117/12.2274522.
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