Philippe Tigreat

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Neural network-based classifiers usually encode the class labels of input data via a completely disjoint code, i.e. a binary vector with only one bit associated with each category. We use coding theory to propose assembly codes where each element is associated with several classes, making for better target vectors. These codes emulate the combination of(More)
Auto-associative memories are a family of algorithms designed for pattern completion. Many of them are based on neural networks, as is the case for Clustered Clique Networks which display competitive pattern retrieval abilities. A sparse variant of these networks was formerly introduced which enables further improved performances. Specific pattern retrieval(More)
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