Keywords

#supervised_learning

Supervised learning

Paradigm in machine learning

Supervised learning (SL) is a paradigm in machine learning where input objects and a desired output value train a model. The training data is processed, building a function that maps new data to expected output values. An optimal scenario will allow for the algorithm to correctly determine output values for unseen instances. This requires the learning algorithm to generalize from the training data to unseen situations in a "reasonable" way. This statistical quality of an algorithm is measured through the so-called generalization error.

Sun 11th

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Supervised learning Self-supervised learning Feature learning Weak supervision Machine learning Unsupervised learning Reinforcement learning Neural network (machine learning) Support vector machine Decision tree learning
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