Batch Processing

Multi-class classifier follows the general workflow described in Usage Model: Training and Prediction.

Training

For a description of the input and output, refer to Usage Model: Training and Prediction.

At the training stage, a multi-class classifier has the following parameters:

Parameter

Default Value

Description

algorithmFPType

float

The floating-point type that the algorithm uses for intermediate computations. Can be float or double.

method

defaultDense

The computation method used by the multi-class classifier. The only training method supported so far is One-Against-One.

training

Pointer to an object of the SVM training class

Pointer to the training algorithm of the two-class classifier. By default, the SVM two-class classifier is used.

nClasses

Not applicable

The number of classes. A required parameter.

Prediction

For a description of the input and output, refer to Usage Model: Training and Prediction.

At the prediction stage, a multi-class classifier has the following parameters:

Parameter

Method

Default Value

Description

algorithmFPType

defaultDense or voteBased

float

The floating-point type that the algorithm uses for intermediate computations. Can be float or double.

pmethod

Not applicable

defaultDense

Available methods for multi-class classifier prediction stage:

  • defaultDense - the method described in [Wu04]
  • voteBasedthe method based on the votes obtained from two-class classifiers.

tmethod

defaultDense or voteBased

training::oneAgainstOne

The computation method that was used to train the multi-class classifier model.

prediction

defaultDense or voteBased

Pointer to an object of the SVM prediction class

Pointer to the prediction algorithm of the two-class classifier. By default, the SVM two-class classifier is used.

nClasses

defaultDense or voteBased

Not applicable

The number of classes. A required parameter.

maxIterations

defaultDense

100

The maximal number of iterations for the algorithm.

accuracyThreshold

defaultDense

1.0e-12

The prediction accuracy.

For more complete information about compiler optimizations, see our Optimization Notice.
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