Unsupervised learning
From Wikipedia, the free encyclopedia
Unsupervised learning is a type of machine learning where manual labels of inputs are not used. It is distinguished from supervised learning approaches which learn how to perform a task, such as classification or regression, using a set of human prepared examples.
One form of unsupervised learning is clustering, which is sometimes not probabilistic. Adaptive resonance theory (ART) allows the number of clusters to vary with problem size and lets the user control the degree of similarity between members of the same clusters by means of a user-defined constant called the vigilance parameter. ART networks are also used for many pattern recognition tasks, such as automatic target recognition and seismic signal processing. The first version of ART was "ART1", developed by Carpenter and Grossberg(1988).
[edit] Bibliography
- Geoffrey Hinton, Terrence J. Sejnowski (editors) (1999) Unsupervised Learning and Map Formation: Foundations of Neural Computation, MIT Press, ISBN 0-262-58168-X (This book focuses on unsupervised learning in neural networks.)
- S. Kotsiantis, P. Pintelas, Recent Advances in Clustering: A Brief Survey, WSEAS Transactions on Information Science and Applications, Vol 1, No 1 (73-81), 2004.
- Richard O. Duda, Peter E. Hart, David G. Stork. Unsupervised Learning and Clustering, Ch. 10 in Pattern classification (2nd edition), p. 571, Wiley, New York, ISBN 0-471-05669-3, 2001.
