ee.Classifier.libsvm

Creates an empty Support Vector Machine classifier.

UsageReturns
ee.Classifier.libsvm(decisionProcedure, svmType, kernelType, shrinking, degree, gamma, coef0, cost, nu, terminationEpsilon, lossEpsilon, oneClass)Classifier
ArgumentTypeDetails
decisionProcedureString, default: "Voting"

The decision procedure to use for classification. Either 'Voting' or 'Margin'. Not used for regression.

svmTypeString, default: "C_SVC"

The SVM type. One of C_SVC, NU_SVC, ONE_CLASS, EPSILON_SVR or NU_SVR.

kernelTypeString, 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)).

shrinkingBoolean, default: true

Whether to use shrinking heuristics.

degreeInteger, default: null

The degree of polynomial. Valid for POLY kernels.

gammaFloat, 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.

coef0Float, default: null

The coef₀ value in the kernel function. Defaults to 0. Valid for POLY and SIGMOID kernels.

costFloat, default: null

The cost (C) parameter. Defaults to 1. Only valid for C-SVC, epsilon-SVR, and nu-SVR.

nuFloat, default: null

The nu parameter. Defaults to 0.5. Only valid for nu-SVC, one-class SVM, and nu-SVR.

terminationEpsilonFloat, default: null

The termination criterion tolerance (e). Defaults to 0.001. Only valid for epsilon-SVR.

lossEpsilonFloat, default: null

The epsilon in the loss function (p). Defaults to 0.1. Only valid for epsilon-SVR.

oneClassInteger, 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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