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RFR generates independent decision trees, and it can work on single-output data as well as multiple regressor tasks. Some of the training examples are missing training labels — yet many machine-learning researchers have found that unlabelled data, when used in conjunction with a small amount of labelled data, can produce a considerable improvement in learning accuracy. Analytical and computational techniques derived from deep-rooted physics of disordered systems can be extended to large-scale problems (including machine learning), e.g., to analyse the weight space of deep neural networks. This subfield, termed “connectionism”, was continued by researchers from other disciplines, including John Hopfield, David Rumelhart, and Geoffrey Hinton.
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