Inferential theory of learning

<p>Inferential Theory of Learning (ITL) is an area of <a href="/facts/Machine_learning/e0w0XJTu">machine learning</a> which describes inferential processes performed by learning agents. ITL has been continuously developed by <a href="/facts/Ryszard_S._Michalski/c2ARsSBn">Ryszard S. Michalski</a>, starting in the 1980s. The first known publication of ITL was in 1983. In the ITL <a href="/facts/Learning/lehEKRjC">learning process</a> is viewed as a search (<a href="/facts/Inference/RBbe95bP">inference</a>) through hypotheses space guided by a specific goal. The results of learning need to be <a href="/facts/Data_storage_device/uCvJ6nrq">stored</a>. Stored information will later be used by the learner for future <a href="/facts/Inference/RBbe95bP">inferences</a>. Inferences are split into multiple categories including <a href="https://www.mli.gmu.edu/index.php/research/inferential-theory-of-learning/">conclusive, deduction, and induction.</a> In order for an inference to be considered complete it was required that all categories must be taken into account. This is how the ITL varies from other machine learning theories like <a href="/facts/Computational_learning_theory/DKMoukIr">Computational Learning Theory</a> and <a href="/facts/Statistical_learning_theory/eK1V4mxN">Statistical Learning Theory</a>; which both use singular forms of inference.
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Inferential theory of learning open-in-new

Inferential theory of learning