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Theoretical results in machine learning mainlyoften dealfocus withon a type of inductive learning calledknown as [[supervised learning]]. In supervised learning, an algorithm is givenprovided samples that arewithe [[Labeled data|labeled]] in some useful waysamples. For exampleinstance, the samples might be descriptions of mushrooms, and thewith labels could beindicating whether orthey notare theedible mushroomsor are ediblenot. The algorithm takesuses these previously labeled samples and uses them to inducecreate a classifier. This classifier is a function that assigns labels to new samples, including samplesthose thatit havehas not been seen previously by the algorithmencountered. The goal of the supervised learning algorithm is to optimize someperformance measure of performancemetrics, such as minimizing the number of mistakes madeerrors on new samples.
In addition to performance bounds, computational learning theory studies the time complexity and feasibility of learning.{{citation needed|date=October 2017}} In
