Abstract
Two neural networks for general mapping problems, backpropagation and counterpropagation, are trained to predict students' grades in Calculus I from placement test responses. The effect of the number of hidden units is investigated. The benefit of including topological structure on the cluster units of a counterpropagation net is illustrated. Noisy data sets are used to train the backpropagation net to improve the ability of the net to generalize.
Original language | English |
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Pages | 3398-3402 |
Number of pages | 5 |
State | Published - 1994 |
Externally published | Yes |
Event | Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) - Orlando, FL, USA Duration: Jun 27 1994 → Jun 29 1994 |
Conference
Conference | Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) |
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City | Orlando, FL, USA |
Period | 06/27/94 → 06/29/94 |