Supervised Learning for Self-Generating Neural Networks
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چکیده
In this paper, supervised learning for Self-Generating Neural Networks (SGNN) method, which was originally developed for the purpose of unsupervised learning, is discussed. An information analytical method is proposed to assign weights to attributes in the training examples if class information is available. This significantly improves the learning speed and the accuracy of the SGNN classiier. The performance of the supervised version of SGNN is analyzed and compared with those of other well-known supervised learning methods.
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تاریخ انتشار 1992