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Compact support vector machine (SVM) for one-class and binary classification

`CompactClassificationSVM`

is a compact version of the
support vector machine (SVM) classifier. The compact classifier does not include the
data used for training the SVM classifier. Therefore, you cannot perform some tasks,
such as cross-validation, using the compact classifier. Use a compact SVM classifier for
tasks such as predicting the labels of new data.

Create a `CompactClassificationSVM`

model from a full, trained
`ClassificationSVM`

classifier by using
`compact`

.

`compareHoldout` | Compare accuracies of two classification models using new data |

`discardSupportVectors` | Discard support vectors for linear support vector machine (SVM) classifier |

`edge` | Find classification edge for support vector machine (SVM) classifier |

`fitPosterior` | Fit posterior probabilities for compact support vector machine (SVM) classifier |

`loss` | Find classification error for support vector machine (SVM) classifier |

`margin` | Find classification margins for support vector machine (SVM) classifier |

`predict` | Classify observations using support vector machine (SVM) classifier |

`update` | Update model parameters for code generation |

[1] Hastie, T., R. Tibshirani, and J. Friedman. *The
Elements of Statistical Learning*, Second Edition. NY: Springer,
2008.

[2] Scholkopf, B., J. C. Platt, J. C. Shawe-Taylor, A. J. Smola,
and R. C. Williamson. “Estimating the Support of a High-Dimensional
Distribution.” *Neural Computation*. Vol. 13, Number 7,
2001, pp. 1443–1471.

[3] Christianini, N., and J. C. Shawe-Taylor. *An Introduction to Support
Vector Machines and Other Kernel-Based Learning Methods*. Cambridge, UK:
Cambridge University Press, 2000.

[4] Scholkopf, B., and A. Smola. *Learning with Kernels: Support Vector
Machines, Regularization, Optimization and Beyond, Adaptive Computation and Machine
Learning.* Cambridge, MA: The MIT Press, 2002.

`ClassificationSVM`

| `compact`

| `discardSupportVectors`

| `fitcsvm`