Experience with adaptive probabilistic neural networks and adaptive general regression neural networks
| Autor: | Harlan M. Romsdahl, Donald F. Specht |
|---|---|
| Rok vydání: | 2002 |
| Předmět: |
Artificial neural network
Computer science Time delay neural network business.industry Deep learning Computer Science::Neural and Evolutionary Computation Probabilistic logic Machine learning computer.software_genre Probabilistic neural network Feedforward neural network Artificial intelligence Types of artificial neural networks Stochastic neural network business computer Computer Science::Databases |
| Zdroj: | Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94). |
| DOI: | 10.1109/icnn.1994.374355 |
| Popis: | By adapting separate smoothing parameters for each dimension, the classification accuracy of the the probabilistic neural network (PNN), and the estimation accuracy of the general regression neural network (GRNN) can both be greatly improved. Accuracy comparisons are given for 28 databases. In addition, the dimensionality of the problem and the complexity of the network can usually be simultaneously reduced. The price to be paid for these benefits is increased training time. > |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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