A higher-order neural network for distortion invariant pattern recognition
Pattern Recognition Letters
Geometric invariance in computer vision
Geometric invariance in computer vision
A note on a higher-order neural network for distortion invariant pattern recognition
Pattern Recognition Letters
Shape quantization and recognition with randomized trees
Neural Computation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Modified high-order neural network for invariant pattern recognition
Pattern Recognition Letters
A Hybrid Higher Order Neural Classifier for handling classification problems
Expert Systems with Applications: An International Journal
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The higher order neural network(HONN) was proved to be able to realize invariantobject recognition. By taking the relationship between input units into account, HONN‘s are superior to other neural models in invariant pattern recognition. However, there are two main problems preventing HONN‘s from practical applications. One is thecombinatorial increase of weight number, that is, as input size increases, the numberof weights in a HONN increases exponentially. The other problem is sensitivity to distortion and noise. In this paper, we described a method, in which by modifying the constraints imposed on the weights in HONN‘s, the performance of a HONN with respect to distortion can be improved considerably.