2 December 2011 Handwritten digits recognition based on immune network
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Proceedings Volume 8004, MIPPR 2011: Pattern Recognition and Computer Vision; 80041A (2011) https://doi.org/10.1117/12.902892
Event: Seventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2011), 2011, Guilin, China
Abstract
With the development of society, handwritten digits recognition technique has been widely applied to production and daily life. It is a very difficult task to solve these problems in the field of pattern recognition. In this paper, a new method is presented for handwritten digit recognition. The digit samples firstly are processed and features extraction. Based on these features, a novel immune network classification algorithm is designed and implemented to the handwritten digits recognition. The proposed algorithm is developed by Jerne's immune network model for feature selection and KNN method for classification. Its characteristic is the novel network with parallel commutating and learning. The performance of the proposed method is experimented to the handwritten number datasets MNIST and compared with some other recognition algorithms-KNN, ANN and SVM algorithm. The result shows that the novel classification algorithm based on immune network gives promising performance and stable behavior for handwritten digits recognition.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yangyang Li, Yunhui Wu, Lc Jiao, Jianshe Wu, "Handwritten digits recognition based on immune network", Proc. SPIE 8004, MIPPR 2011: Pattern Recognition and Computer Vision, 80041A (2 December 2011); doi: 10.1117/12.902892; https://doi.org/10.1117/12.902892
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