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177

ANNs can be used for learning. Learning can comprise using a number of observations to find a function which solves a predetermined or desired task in an optimal sense, e.g. an optimal outcome. Learning can be supervised learning, unsupervised learning and reinforcement learning. Supervised learning can use one or more sets of example pairs and the goal can be, for example, to find a function in an allowed class of functions that matches the examples. Pattern recognition, classification, and regression can be part of supervised learning. Supervised learning can use informational fuzzy networks, random forests, nearest neighbor algorithms, logistic model tree, and other algorithms. Supervised learning can use statistical classification, including, for example, decision trees, Bayesian networks, and/or linear classifiers.

Added by DJM Jan 2024