Handwritten Digits Recognition based on immune network
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 Jernes 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.
Handwritten digits recognition immune network classification MNIST database pattern recognition
Yangyang Li Yunhui Wu Lc Jiao Jianshe Wu
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education ofChina, X Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China,
国际会议
桂林
英文
1-8
2011-11-01（万方平台首次上网日期，不代表论文的发表时间）