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Machine learning Wikipedia, the free encyclopedia.
Another example is learning to play a game by playing against an opponent. Between supervised and unsupervised learning is semi-supervised learning, where the teacher gives an incomplete training signal: a training set with some often many of the target outputs missing. Transduction is a special case of this principle where the entire set of problem instances is known at learning time, except that part of the targets are missing. A support vector machine is a classifier that divides its input space into two regions, separated by a linear boundary. Here, it has learned to distinguish black and white circles. Among other categories of machine learning problems, learning to learn learns its own inductive bias based on previous experience.
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