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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 |

`incrementalLearner` | Convert binary classification support vector machine (SVM) model to incremental learner |

`lime` | Local interpretable model-agnostic explanations (LIME) |

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

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

`partialDependence` | Compute partial dependence |

`plotPartialDependence` | Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots |

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

`shapley` | Shapley values |

`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`