Table Of Contents
Table Of Contents


mxnet.ndarray.SVMOutput(data=None, label=None, margin=_Null, regularization_coefficient=_Null, use_linear=_Null, out=None, name=None, **kwargs)

Computes support vector machine based transformation of the input.

This tutorial demonstrates using SVM as output layer for classification instead of softmax:

  • data (NDArray) – Input data for SVM transformation.
  • label (NDArray) – Class label for the input data.
  • margin (float, optional, default=1) – The loss function penalizes outputs that lie outside this margin. Default margin is 1.
  • regularization_coefficient (float, optional, default=1) – Regularization parameter for the SVM. This balances the tradeoff between coefficient size and error.
  • use_linear (boolean, optional, default=0) – Whether to use L1-SVM objective. L2-SVM objective is used by default.
  • out (NDArray, optional) – The output NDArray to hold the result.

out – The output of this function.

Return type:

NDArray or list of NDArrays