Creates an empty Support Vector Machine classifier.
| Usage | Returns |
|---|---|
ee.Classifier.libsvm(decisionProcedure, svmType, kernelType, shrinking, degree, gamma, coef0, cost, nu, terminationEpsilon, lossEpsilon, oneClass) | Classifier |
| Argument | Type | Details |
|---|---|---|
decisionProcedure | String, default: "Voting" | The decision procedure to use for classification. Either 'Voting' or 'Margin'. Not used for regression. |
svmType | String, default: "C_SVC" | The SVM type. One of C_SVC, NU_SVC, ONE_CLASS, EPSILON_SVR or NU_SVR. |
kernelType | String, default: "LINEAR" | The kernel type. One of LINEAR (u′×v), POLY ((γ×u′×v + coef₀)ᵈᵉᵍʳᵉᵉ), RBF (exp(-γ×|u-v|²)) or SIGMOID (tanh(γ×u′×v + coef₀)). |
shrinking | Boolean, default: true | Whether to use shrinking heuristics. |
degree | Integer, default: null | The degree of polynomial. Valid for POLY kernels. |
gamma | Float, default: null | The gamma value in the kernel function. Defaults to the reciprocal of the number of features. Valid for POLY, RBF and SIGMOID kernels. |
coef0 | Float, default: null | The coef₀ value in the kernel function. Defaults to 0. Valid for POLY and SIGMOID kernels. |
cost | Float, default: null | The cost (C) parameter. Defaults to 1. Only valid for C-SVC, epsilon-SVR, and nu-SVR. |
nu | Float, default: null | The nu parameter. Defaults to 0.5. Only valid for nu-SVC, one-class SVM, and nu-SVR. |
terminationEpsilon | Float, default: null | The termination criterion tolerance (e). Defaults to 0.001. Only valid for epsilon-SVR. |
lossEpsilon | Float, default: null | The epsilon in the loss function (p). Defaults to 0.1. Only valid for epsilon-SVR. |
oneClass | Integer, default: null | The class of the training data on which to train in a one-class SVM. Defaults to 0. Only valid for one-class SVM. Possible values are 0 and 1. The classifier output is binary (0/1) and will match this class value for the data determined to be in the class. |
Examples
JavaScript
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Python
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