Capabilities and Limitations of Feedforward Neural Networks with Multilevel Neurons
نویسندگان
چکیده
This paper proposes a multilevel logic approach to output coding using multilevel neurons in the output layer. Training convergence for a single multilevel perceptron is considered. It has been found that a multilevel neural network classifier with a reduced number of outputs is often able to learn faster and requires fewer weights. Concepts are illustrated with an example of a digit classifier.
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