Machine learning algorithms can be organized into a taxonomy based on the desired outcome of the algorithm.
- Supervised learning generates a function that maps inputs to desired outputs (also called labels, because they are often provided by human experts labeling the training examples). For example, in a classification problem, the learner approximates a function mapping a vector into classes by looking at input-output examples of the function.
- Unsupervised learning models a set of inputs, like clustering. See also data mining and knowledge discovery.
- Semi-supervised learning combines both labeled and unlabeled examples to generate an appropriate function or classifier.
- Reinforcement learning learns how to act given an observation of the world. Every action has some impact in the environment, and the environment provides feedback in the form of rewards that guides the learning algorithm.
- Transduction, or transductive inference, tries to predict new outputs on specific and fixed (test) cases from observed, specific (training) cases.
- Learning to learn learns its own inductive bias based on previous experience.
Read more about this topic: Machine Learning
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