Daniel Prelinger

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Prediction problems are among the most common learning problems for neural networks (e.g. in the context of time series prediction, control, etc.). With many such problems, however, perfect prediction is inherently impossible. For such cases we present novel unsupervised systems that learn to classify patterns such that the classiications are predictable(More)
Assume we are given a set of pairs of patterns. We know that both patterns of each pair belong to the same class. We do not know in advance, however , anything about the nature of the classes, which features are characteristic for each class, how many classes there are, and which patterns belong to which class. We present a novel unsupervised neural system(More)
Prediction problems are among the most common learning problems for neural networks (e.g. in the context of time series prediction, control, etc.). With many such problems, however, perfect prediction is inherently impossible. For such cases we present novel unsupervised systems that learn to classify patterns such that the classiications are predictable(More)
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