Function approximation algorithms
function approximation algorithms
include connectionist and statistical techniques of machine learning. The idea is that machine learning means learning, from a number of examples or instances or training patterns, to compute a function which has as its arguments variables corresponding to the input part of the training pattern(s), and has as its output variables corresponding to the output part of the training patterns, which maps the input part of each training pattern to its output part. The hope is that the function will interpolate / generalize from the training patterns, so that it will produce reasonable outputs when given other inputs.
See also symbolic learning algorithms.
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