@inproceedings{oai:kanazawa-u.repo.nii.ac.jp:00007506,
 author = {Hara, Kazuyuki and Nakayama, Kenji},
 book = {IEEE International Conference on Neural Networks - Conference Proceedings},
 month = {Jun},
 note = {A training data reduction method for a multilayer neural network (MLNN) is proposed in this paper. This method reduce the data by selecting the minimum number of training data that guarantee generality of the MLNN. For this purpose, two methods are used. One of them is a pairing method which selects the training data by finding the nearest data of the different classes. Data along the class boundary in data space can be selected. The other method is a training method, which used a semi-optimum MLNN in a training process. Since the MLNN classify data based on the distance from the network boundary, the selected data can locate close to the class boundary. So, if the semi-optimum MLNN did not select data from class boundary, pairing method can select them. The proposed methods can be applied to both off-line training and on-line training. The proposed method is also investigated through computer simulation., 金沢大学理工研究域電子情報学系},
 pages = {436--441},
 publisher = {IEEE(Institute of Electrical and Electronics Engineers)},
 title = {Selection of minimum training sata for generalization and on-line training by multilayer neural networks},
 volume = {1},
 year = {1996}
}