Classifying Scaled-Turned-Shifted Objects with Optimal Pixel-to-Scale-Turn-Shift Standard Deviations Ratio in Training 2-Layer Perceptron on Scaled-Turned-Shifted 4800-Featured Objects under Normally Distributed Feature Distortion
The problem of classifying diversely distorted objects is considered. The classifier is a 2-layer perceptron capable of classifying greater amounts of objects in a unit of time. This is an advantage of the 2-layer perceptron over more complex neural networks like the neocognitron, the convolutional...
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Format: | Article |
Language: | English |
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Sciendo
2017-12-01
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Series: | Electrical, Control and Communication Engineering |
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Online Access: | https://doi.org/10.1515/ecce-2017-0007 |