Daniel Prelinger

Learn 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)
Neural networks have proven poor at learning the structure in complex and extended temporal sequences in which contingencies among elements can span long time lags. The principle of history compression 18] provides a means of transforming long sequences with redundant information into equivalent shorter sequences; the shorter sequences are more easily(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)
  • 1