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วารสารราชบััณฑิิตยสภา
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ศาสตราจารย์์ ดร.ธนารักษ์์ ธีระมั่ั�นคง และนางสาวเบญจพรรณ สมั่ณะ 189
Cortes, C. & Vapnik, V. (1995). Machine Learning. (pp. 273–297). New Jersey, United
States: Kluwer Academic Publishers-Plenum Publishers.
Gang Wu, S., Sheng Bao, F., You Xu, E., Wang, Y., Chang, Y. & Xiang, Q. (2007). A Leaf
Recognition Algorithm for Plant Classification Using Probabilistic Neural
Network. In 2007 IEEE International Symposium on Signal Processing and
Information Technology. Giza, Egypt: IEEE. [online]. from https://ieeexplore.
ieee.org/abstract/document/4458016
Gao, M., Lin, L. & O. Sinnott, R. (2017). A Mobile Application for Plant Recognition
through Deep Learning. In 2017 IEEE 13 International Conference on
th
e-Science (e-Science). Auckland, New Zealand: IEEE. [online]. from https://
ieeexplore.ieee.org/document/8109120.
Glorot, X. & Bengio, Y. (2010). Understanding the difficulty of training deep feedforward
neural networks. In The International Conference on Artificial Intelligence
and Statistics (AISTATS’10). [online]. from http://citeseerx.ist.psu.edu/
viewdoc/summary?doi=10.1.1.207.2059.
He, K., Zhang, X., Ren, S. & Sun, J. (2016). Deep Residual Learning for Image Recognition.
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
770–778.
Ioffe, S. & Szegedy, C. (2015). Batch Normalization: Accelerating Deep Network Training
by Reducing Internal Covariate Shift. Corr. [online]. from http://arxiv.org/abs/
1502.03167.
Kadir, A., E. Nugroho, L., Susanto, A. & Santosa, P. (2011). Neural Network Application
on Foliage Plant Identification. International Journal of Computer
Applications, 29(9), 15–22. doi: 10.5120/3592-4981.
Krizhevsky, A., Sutskever, I. & E. Hinton, G. (2012). ImageNet classification with deep
convolutional neural networks. In NIPS’12 Proceedings of the 25
th
International Conference on Neural Information Processing Systems.
Lake Tahoe, Nevada. [online]. from https://dl.acm.org/citation.cfm?id=2999257
