Prototype methods

Short description: Method of machine learning

Prototype methods are machine learning methods that use data prototypes.^[1] A data prototype is a data value that reflects other values in its class,^[2] e.g., the centroid in a K-means clustering problem.

Methods

The following are some prototype methods^[3]

K-means clustering
Learning vector quantization (LVQ)
Gaussian mixtures

Related Methods

While K-nearest neighbor's does not use prototypes, it is similar to prototype methods like K-means clustering.^[4]

References

↑ Hastie, Trevor. The elements of statistical learning : data mining, inference, and prediction. Tibshirani, Robert,, Friedman, J. H. (Jerome H.) (Second ed.). New York. pp. 459. ISBN 9780387848570. OCLC 300478243.
↑ Molnar, Christoph. 6.3 Prototypes and Criticisms | Interpretable Machine Learning. https://christophm.github.io/interpretable-ml-book/proto.html.
↑ Hastie, Trevor. The elements of statistical learning : data mining, inference, and prediction. Tibshirani, Robert,, Friedman, J. H. (Jerome H.) (Second ed.). New York. pp. 459–463. ISBN 9780387848570. OCLC 300478243.
↑ Hastie, Trevor. The elements of statistical learning : data mining, inference, and prediction. Tibshirani, Robert,, Friedman, J. H. (Jerome H.) (Second ed.). New York. pp. 465. ISBN 9780387848570. OCLC 300478243.

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[1] Hastie, Trevor. The elements of statistical learning : data mining, inference, and prediction. Tibshirani, Robert,, Friedman, J. H. (Jerome H.) (Second ed.). New York. pp. 459. ISBN 9780387848570. OCLC 300478243.

[2] Molnar, Christoph. 6.3 Prototypes and Criticisms | Interpretable Machine Learning. https://christophm.github.io/interpretable-ml-book/proto.html.

[3] Hastie, Trevor. The elements of statistical learning : data mining, inference, and prediction. Tibshirani, Robert,, Friedman, J. H. (Jerome H.) (Second ed.). New York. pp. 459–463. ISBN 9780387848570. OCLC 300478243.

[4] Hastie, Trevor. The elements of statistical learning : data mining, inference, and prediction. Tibshirani, Robert,, Friedman, J. H. (Jerome H.) (Second ed.). New York. pp. 465. ISBN 9780387848570. OCLC 300478243.

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