Computational learning theory: Difference between revisions

Revision as of 15:41, 21 August 2020

Revision as of 02:06, 29 October 2020

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==Overview==

Theoretical results in machine learning mainly deal with a type of inductive learning called [[supervised learning]]. In supervised learning, an algorithm is given samples that are labeled in some useful way. For example, the samples might be descriptions of mushrooms, and the labels could be whether or not the mushrooms are edible. The algorithm takes these previously labeled samples and uses them to induce a classifier. This classifier is a function that assigns labels to samples, including samples that have not been seen previously by the algorithm. The goal of the supervised learning algorithm is to optimize some measuremeasures of performance such as minimizing the number of mistakes made 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