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The echo-state-network approach for training recurrent neural networks can yield good results. However, the results depend on the experience of neural network design. It usually requires multiple tests and random chances. Through our study of the effects of spectral radius of the internal weight matrix on the training results, we propose to develop a method that can improve the echo-state network...
Based on the theories of the intrusion trapping and natural language understanding, oriented e-government affairs security issues, this paper proposed a content-based self-feedback model at the point of attackers. By this model, the concrete information under attacking can be focused and the attack methods would be ignored in a standard honey trap. With the supporting of honey nets, The target sensitivity...
Most of microarray data sets are imbalanced, i.e. the number of positive examples is much less than that of negative, which will hurt performance of classifiers when it is used for tumor classification. Though it is critical, few previous works paid attention to this problem. Here we propose embedded gene selection with two algorithms i.e. EGSEE (Embedded Gene Selection for EasyEnsemble) and EGSIEE...
This paper presents an implementation of incremental tumor diagnosis algorithm (ITDA) on microarray data for improving diagnostic accuracy of tumor. A classifier (BP or KNN) was used in the algorithm to estimate confidences of a new unlabeled sample in different classes. When one confidence is higher than the threshold, the sample will be labeled; otherwise, the sample will be diagnosed by medical...
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