| Status | 已發表Published |
| Instant learning for supervised learning neural networks: a rank-expansion algorithm | |
| Philip Chen C.L.; Luo Jiyang | |
| 1994-12-01 | |
| Source Publication | IEEE International Conference on Neural Networks - Conference Proceedings
 |
| Volume | 2 |
| Pages | 781-801 |
| Abstract | An one-hidden layer neural network architecture is presented. An instant learning algorithm is given to decide the weights of a supervised learning neural network. For an n dimensional, N-pattern training set, a maximum of N-r hidden nodes are required to learn all the patterns within a given precision (where r is the rank, usually the dimension, of the input patterns). Using the inverse of activation function, the algorithms transfer the output to the hidden layer, add bias nodes to the input, expand the rank of input dimension. The proposed architecture and algorithm can obtain either exact solution or minimum least square error of the inverse activation of the output. The learning error only occurs when applying the inverse of activation function. Usually, this can be controlled by the given precision. Several examples show the very promising result. |
| URL | View the original |
| Language | 英語English |
| Fulltext Access | |
| Document Type | Conference paper |
| Collection | University of Macau |
| Affiliation | Wright State University |
| Recommended Citation GB/T 7714 | Philip Chen C.L.,Luo Jiyang. Instant learning for supervised learning neural networks: a rank-expansion algorithm[C], 1994, 781-801. |
| APA | Philip Chen C.L.., & Luo Jiyang (1994). Instant learning for supervised learning neural networks: a rank-expansion algorithm. IEEE International Conference on Neural Networks - Conference Proceedings, 2, 781-801. |
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