A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Comparing support vector machines with Gaussian kernels to radialbasis function classifiers
IEEE Transactions on Signal Processing
Recent Advances in the Neocognitron
Neural Information Processing
Recent Studies Around the Neocognitron
Neural Information Processing
Training multi-layered neural network neocognitron
Neural Networks
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This paper proposes a powerful algorithm for pattern recognition, which uses interpolating vectors for classifying patterns. Labeled reference vectors in a multi-dimensional feature space are first produced by a kind of competitive learning. We then assume a situation where virtual vectors, called interpolating vectors, are densely placed along line segments connecting all pairs of reference vectors of the same label. From these interpolating vectors, we choose the one that has the largest similarity to the test vector. Its label shows the result of pattern recognition. In practice, we can get the same result with a simpler process. We applied this method to the neocognitron for handwritten digit recognition and reduced the error rate from 1.52% to 1.02% for a blind test set of 5000 digits.