Supervised Learning

Abstract

Supervised learning uses a labeled training set of typical objects. The learning refers to some form of adaptation of the classification algorithm to achieve a better response, which will help in classifying the unknown test set. Parametric methods use some form of probability distribution, while non-parametric methods use arbitrary distributions of unknown densities. Parametric learning relies on Bayesian decision theory, and can be easily linked to the concepts of discriminant functions and decision boundaries. [Non-parametric methods include artificial neural networks (ANNs) and support vector machines (SVMs).]