@inproceedings{525af3e62dfe43f6ba9fb3ae4affc06c,
title = "Kernel based principal component for recognizing handwritten numbers",
abstract = "We implement kernel-based principal component analysis to recognize handwritten numbers. Then we analyze the relationship of each numeral type's eigenvalue and eigenvector with its recognition rate. We also present a modified algorithm to improve the robustness as well as the efficiency of the recognition method by employing the secondary training and detection methods from the perspective of nature of kernel function. This method can solve the problem of low recognition rate of a small number of scribbled characters at both low time cost and space complexity. Experiments using 1000 to 5000 test samples all show that our method can achieve 97.8\% to 99.0\% recognition accuracy.",
author = "Xiaoxiao Song and Songhua Xu and Miranker, \{Willard L.\}",
year = "2010",
doi = "10.1109/IJCNN.2010.5596733",
language = "英语",
isbn = "9781424469178",
series = "Proceedings of the International Joint Conference on Neural Networks",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010",
note = "2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010 ; Conference date: 18-07-2010 Through 23-07-2010",
}