Tube Inner Circumference State Classification Optimization by Using Artificial Neural Networks, Random Forest and Support Vector Machines Algorithms
Author:
Publisher
Springer Singapore
Link
http://link.springer.com/content/pdf/10.1007/978-981-13-9190-3_59
Reference10 articles.
1. Hung, C.-W., Jiang, J.-G., Wu, H.-H.P., Mao, W.-L.: An automated optical inspection system for a tube inner circumference state identification. In: ICAROB (2018)
2. Yuanyuan, S., Yongming, W., Lili, G., Zhongsong, M., Shan, J.: The comparison of optimizing SVM by GA and grid search. In: 2017 13th IEEE International Conference on Electronic Measurement & Instruments (ICEMI), pp. 354–360, Yangzhou (2017). https://doi.org/10.1109/icemi.2017.8265815
3. Ross, J.: Fuzzy Logic with Engineering Application, 3rd edn, pp. 179–183. Wiley-Blackwell (2010)
4. Cross entropy available. http://en.wikipedia.org/wiki/Cross_entropy
5. Yusong, P.O.: Generalization of the cross-entropy error function to improve the error backpropagation algorithm. In: Proceedings of International Conference on Neural Networks (ICNN’97), vol. 3, pp. 1856–1861, Houston, TX, USA (1997). https://doi.org/10.1109/icnn.1997.614181
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