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oneClassSvm: oneClassSvm

  • Article

Creates a list containing the function name and arguments to train a OneClassSvm model with rxEnsemble.

Usage

  oneClassSvm(cacheSize = 100, kernel = rbfKernel(), epsilon = 0.001,
    nu = 0.1, shrink = TRUE, ...)

Arguments

cacheSize

The maximal size in MB of the cache that stores the training data. Increase this for large training sets. The default value is 100 MB.

kernel

A character string representing the kernel used for computing inner products. For more information, see maKernel. The following choices are available:

  • rbfKernel(): Radial basis function kernel. Its parameter representsgamma in the term exp(-gamma|x-y|^2. If not specified, it defaults to 1 divided by the number of features used. For example, rbfKernel(gamma = .1). This is the default value.
  • linearKernel(): Linear kernel.
  • polynomialKernel(): Polynomial kernel with parameter names a, bias, and deg in the term (a*<x,y> + bias)^deg. The bias, defaults to 0. The degree, deg, defaults to 3. If a is not specified, it is set to 1 divided by the number of features. For example, maKernelPoynomial(bias = 0, deg = `` 3).
  • sigmoidKernel(): Sigmoid kernel with parameter names gamma and coef0 in the term tanh(gamma*<x,y> + coef0). gamma, defaults to 1 divided by the number of features. The parameter coef0 defaults to 0. For example, sigmoidKernel(gamma = .1, coef0 = 0).

epsilon

The threshold for optimizer convergence. If the improvement between iterations is less than the threshold, the algorithm stops and returns the current model. The value must be greater than or equal to .Machine$double.eps. The default value is 0.001.

nu

The trade-off between the fraction of outliers and the number of support vectors (represented by the Greek letter nu). Must be between 0 and 1, typically between 0.1 and 0.5. The default value is 0.1.

shrink

Uses the shrinking heuristic if TRUE. In this case, some samples will be "shrunk" during the training procedure, which may speed up training. The default value is TRUE.

...

Additional arguments to be passed directly to the Microsoft Compute Engine.