# Pattern recognition

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Pattern recognition is a branch of machine learning that focuses on the recognition of patterns and regularities in data, although is in some cases considered to be nearly synonymous with machine learning.[1] Pattern recognition systems are in many cases trained from labeled "training" data (supervised learning), but when no labeled data are available other algorithms can be used to discover previously unknown patterns (unsupervised learning).

The terms pattern recognition, machine learning, data mining and knowledge discovery in databases (KDD) are hard to separate, as they largely overlap in their scope. Machine learning is the common term for supervised learning methods{{ safesubst:#invoke:Unsubst||$N=Dubious |date=__DATE__ |$B= {{#invoke:Category handler|main}}[dubious ] }} and originates from artificial intelligence, whereas KDD and data mining have a larger focus on unsupervised methods and stronger connection to business use. Pattern recognition has its origins in engineering, and the term is popular in the context of computer vision: a leading computer vision conference is named Conference on Computer Vision and Pattern Recognition. In pattern recognition, there may be a higher interest to formalize, explain and visualize the pattern; whereas machine learning traditionally focuses on maximizing the recognition rates. Yet, all of these domains have evolved substantially from their roots in artificial intelligence, engineering and statistics; and have become increasingly similar by integrating developments and ideas from each other.

### Classification algorithms (supervised algorithms predicting categorical labels)

{{#invoke:main|main}} Parametric:[15]

Nonparametric:[16]

### Clustering algorithms (unsupervised algorithms predicting categorical labels)

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### Ensemble learning algorithms (supervised meta-algorithms for combining multiple learning algorithms together)

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Unsupervised:

### Real-valued sequence labeling algorithms (predicting sequences of real-valued labels)

{{#invoke:main|main}} Supervised (?):

### Regression algorithms (predicting real-valued labels)

{{#invoke:main|main}} Supervised:

Unsupervised:

Supervised:

Unsupervised:

## References

This article is based on material taken from the Free On-line Dictionary of Computing prior to 1 November 2008 and incorporated under the "relicensing" terms of the GFDL, version 1.3 or later.

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3. Isabelle Guyon Clopinet, André Elisseeff (2003). An Introduction to Variable and Feature Selection. The Journal of Machine Learning Research, Vol. 3, 1157-1182. Link
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7. Richard O. Duda, Peter E. Hart, David G. Stork (2001) Pattern classification (2nd edition), Wiley, New York, ISBN 0-471-05669-3
8. R. Brunelli, Template Matching Techniques in Computer Vision: Theory and Practice, Wiley, ISBN 978-0-470-51706-2, 2009
9. Neural Networks for Face Recognition Companion to Chapter 4 of the textbook Machine Learning.
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12. Assuming known distributional shape of feature distributions per class, such as the Gaussian shape.
13. No distributional assumption regarding shape of feature distributions per class.

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