The algorithm takes these previously labeled samples and uses them to induce a classifier.
The goal of the supervised learning algorithm is to optimize some measure of performance such as minimizing the number of mistakes made on new samples.
[citation needed] There are two kinds of time complexity results: Negative results often rely on commonly believed, but yet unproven assumptions,[citation needed] such as: There are several different approaches to computational learning theory based on making different assumptions about the inference principles used to generalise from limited data.
[citation needed] The different approaches include: While its primary goal is to understand learning abstractly, computational learning theory has led to the development of practical algorithms.
For example, PAC theory inspired boosting, VC theory led to support vector machines, and Bayesian inference led to belief networks.