stands for Digital Object Identifier
and is the unique identifier for objects on the internet. It can be used to create persistent link and to cite articles.
Using DOI as a persistent link
To create a persistent link, add「http://dx.doi.org/」
before a DOI.
For instance, if the DOI of an article is 10.5297/ser.1201.002 , you can link persistently to the article by entering the following link in your browser: http://dx.doi.org/ 10.5297/ser.1201.002 。
The DOI link will always direct you to the most updated article page no matter how the publisher changes the document's position, avoiding errors when engaging in important research.
Cite a document with DOI
When citing references, you should also cite the DOI if the article has one. If your citation guideline does not include DOIs, you may cite the DOI link.
DOIs allow accurate citations, improve academic contents connections, and allow users to gain better experience across different platforms. Currently, there are more than 70 million DOIs registered for academic contents. If you want to understand more about DOI, please visit airiti DOI Registration （ doi.airiti.com ） 。
鄭育淵 , Masters Advisor：歐陽振森
極限學習機 ； 類神經網路 ； 模糊系統 ； 模糊推論系統 ； 遞迴式奇異值分解 ； 線上學習 ； 模糊極限學習機 ； extreme learning machine ； artificial neural network ； fuzzy system ； fuzzy inference system ； recursive singular value decomposition ； online learning ； fuzzy extreme learning machine
- G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, “Extreme learning machine: a new learning scheme of feedforward neural networks,” 2004 IEEE International Joint Conference on Neural Networks, vol. 2, pp. 985-990, 2004.
- S. Tamura and M. Tateishi, “Capabilities of a four-layered feedforward neural network: four layers versus three,” IEEE Transactions on Neural Networks, vol. 8, no. 2, pp. 251-255, 1997.
- G.-B. Huang, “Learning capability and storage capacity of two-hidden-layer feedforward networks,” IEEE Transactions on Neural Networks, vol. 14, no. 2, pp. 274-281, 2003.
- G.-B. Huang, “Real-time learning capability of neural networks,” IEEE Journals & Magazines, vol.17, no. 4, pp. 863-878, 2006.
- P. L. Bartlett, “The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network,” IEEE Transactions on Information Theory, vol. 44, no. 2, pp. 525-536, Mar. 1998.
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