Page "Pattern recognition" Paragraph 3

Pattern recognition is generally categorized according to the type of learning procedure used to generate the output value.

Supervised learning assumes that a set of training data ( the training set ) has been provided, consisting of a set of instances that have been properly labeled by hand with the correct output.

A learning procedure then generates a model that attempts to meet two sometimes conflicting objectives: Perform as well as possible on the training data, and generalize as well as possible to new data ( usually, this means being as simple as possible, for some technical definition of " simple ", in accordance with Occam's Razor ).

Unsupervised learning, on the other hand, assumes training data that has not been hand-labeled, and attempts to find inherent patterns in the data that can then be used to determine the correct output value for new data instances.

A combination of the two that has recently been explored is semi-supervised learning, which uses a combination of labeled and unlabeled data ( typically a small set of labeled data combined with a large amount of unlabeled data ).

Note that in cases of unsupervised learning, there may be no training data at all to speak of ; in other words, the data to be labeled is the training data.