Classifier (mathematics): Difference between revisions
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In [[mathematics]], a '''classifier''' is a mapping from a (discrete or continuous) [[feature space]] ''X'' to a discrete set of labels ''Y''. |
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#REDIRECT [[Statistical classification]] |
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Classifiers may either be fixed classifiers or learning classifiers, and learning classifiers may in turn be divided into supervised and unsupervised learning classifiers. |
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There are a number of different types of classifier technologies: |
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* Bayes classifers: |
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** [[Naive Bayes classifier]] |
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** [[Optimal Bayes classifier]] |
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* [[Support vector machine]]s |
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* [[k-nearest neighbor]] classifiers |
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* [[neural network]] classifiers |
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* [[linear classifier]]s |
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** [[Fisher's linear discriminant]] |
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* [[quadratic classifier]]s |
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Classifiers are used in many practical applications, including [[optical character recognition]], [[speech recognition]] and [[handwriting recognition]], [[biometric identification]], [[document classification]] and Internet [[search engine]]s. |
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== See also == |
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* [[Viterbi algorithm]] |
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* [[Dynamic time warping]] |
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* [[Machine learning]] |
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{{math-stub}} |
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[[Category:Pattern recognition]] |
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[[Category:Classification|*]] |
Revision as of 09:25, 14 March 2005
In mathematics, a classifier is a mapping from a (discrete or continuous) feature space X to a discrete set of labels Y.
Classifiers may either be fixed classifiers or learning classifiers, and learning classifiers may in turn be divided into supervised and unsupervised learning classifiers.
There are a number of different types of classifier technologies:
- Bayes classifers:
- Support vector machines
- k-nearest neighbor classifiers
- neural network classifiers
- linear classifiers
- quadratic classifiers
Classifiers are used in many practical applications, including optical character recognition, speech recognition and handwriting recognition, biometric identification, document classification and Internet search engines.