Abstract
It would be a mistake to think that machine learning always requires examples with class labels. Far from it! Useful information can be gleaned even from examples whose classes are not known. This is sometimes called unsupervised learning, in contrast to the term supervised learning which is used when talking about induction from pre-classified examples.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Machine learning professionals sometimes avoid the term “center” which might imply mathematical properties that are for the specific needs of cluster analysis largely irrelevant.
- 2.
For instance, the section on RBF networks denoted these centers by μ i ’s.
- 3.
In statistics, and in neural networks, scientists often use the term feature instead of attribute.
- 4.
Recall that one epoch means that all training examples have been presented once.
- 5.
A bulldozer is more powerful than a spade, and yet the gardener prefers the spade most of the time.
References
Ball, G. H. & Hall, D. J. (1965). ISODATA, a novel method of data analysis and clasification. Technical Report of the Standford University, Stanford, CA
Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43, 59–69
McQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the 5th Berkeley symposium on mathematical statistics and probability, Berkeley (pp. 281–297).
Murty, M. N. & Krishna, G. (1980). A computationally efficient technique for data clustering. Pattern Recognition, 12, 153–158.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this chapter
Cite this chapter
Kubat, M. (2017). Unsupervised Learning. In: An Introduction to Machine Learning. Springer, Cham. https://doi.org/10.1007/978-3-319-63913-0_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-63913-0_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-63912-3
Online ISBN: 978-3-319-63913-0
eBook Packages: Computer ScienceComputer Science (R0)