Advertisement
Published in:
2013 | OriginalPaper | Chapter
5. Supervised Learning
Author : Geoff Dougherty
Published in: Pattern Recognition and Classification
Publisher: Springer New York
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
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).]