Randomized Model Structure Selection Approach for Extreme Learning Machine Applied to Acid Sulfate Soil Detection
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-21678-7_4
Reference11 articles.
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3. Beucher, A., Adhikari, K., Breuning-Madsen, H., Greve, M., Österholm, P., Fröjdö, S., Jensen, N., Greve, M.: Mapping potential acid sulfate soils in denmark using legacy data and lidar-based derivatives. Geoderma 308, 363–372 (2017)
4. Beucher, A., Møller, A., Greve, M.: Artificial neural networks and decision tree classification for predicting soil drainage classes in denmark. Geoderma 352, 351–359 (2019)
5. Beucher, A., Fröjdö, S., Österholm, P., Martinkauppi, A., Edén, P.: Fuzzy logic for acid sulfate soil mapping: application to the southern part of the finish coastal areas. Geoderma 226–227, 21–30 (2014)
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Importance of the Activation Function in Extreme Learning Machine for Acid Sulfate Soil Classification;Proceedings in Adaptation, Learning and Optimization;2024
2. Improving prediction accuracy for acid sulfate soil mapping by means of variable selection;Frontiers in Environmental Science;2023-07-14
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