Generalizations of Supervised Learning
There are several ways in which the standard supervised learning problem can be generalized:
- Semi-supervised learning: In this setting, the desired output values are provided only for a subset of the training data. The remaining data is unlabeled.
- Active learning: Instead of assuming that all of the training examples are given at the start, active learning algorithms interactively collect new examples, typically by making queries to a human user. Often, the queries are based on unlabeled data, which is a scenario that combines semi-supervised learning with active learning.
- Structured prediction: When the desired output value is a complex object, such as a parse tree or a labeled graph, then standard methods must be extended.
- Learning to rank: When the input is a set of objects and the desired output is a ranking of those objects, then again the standard methods must be extended.
Read more about this topic: Supervised Learning
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