Neural networks are competitive tools for classification problems. In this context, a hint is any piece of prior side information about the classification. Common examples are monotonicity hints. The present paper focuses on learning vector quantization neural networks and gives a simple, however effective, technique, which guarantees that the predictions of the network obey the required monotonicity properties in a strict fashion. The method is based on a proper modification of the Euclidean distance between input and codebook vectors.
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