Supervised Learning is the Machine Learning task of inferring a function from training dataset.

The training data consist of a set of training examples.

In Supervised Learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal).

A Supervised Learning algorithm analyzes the training dataset and produces an inferred function, which can be used for mapping new examples. An optimal scenario will allow for the algorithm to correctly determine the classification for unseen instances. This requires the learning algorithm to generalize from the training dataset to unseen situations in a "reasonable" way (see inductive bias).

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« This page (revision-5) was last changed on 25-Nov-2017 10:49 by jim