ai.onnx.ml - SVMRegressor#
SVMRegressor - 1 (ai.onnx.ml)#
Version
name: SVMRegressor (GitHub)
domain: ai.onnx.ml
since_version: 1
function: False
support_level: SupportType.COMMON
shape inference: False
This version of the operator has been available since version 1 of domain ai.onnx.ml.
Summary
Support Vector Machine regression prediction and one-class SVM anomaly detection.
Attributes
coefficients: Support vector coefficients.
kernel_params: List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel.
kernel_type: The kernel type, one of ‘LINEAR,’ ‘POLY,’ ‘RBF,’ ‘SIGMOID’. Default value is
'LINEAR'.n_supports: The number of support vectors. Default value is
0.one_class: Flag indicating whether the regression is a one-class SVM or not. Default value is
0.post_transform: Indicates the transform to apply to the score. <br>One of ‘NONE,’ ‘SOFTMAX,’ ‘LOGISTIC,’ ‘SOFTMAX_ZERO,’ or ‘PROBIT.’ Default value is
'NONE'.rho:
support_vectors: Chosen support vectors
Inputs
X (heterogeneous) - T: Data to be regressed.
Outputs
Y (heterogeneous) - tensor(float): Regression outputs (one score per target per example).
Type Constraints
T in ( tensor(double), tensor(float), tensor(int32), tensor(int64) ): The input type must be a tensor of a numeric type, either [C] or [N,C].
Examples