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	double	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.
prediction	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	Not applicable	The number of classes, a required parameter.

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	Default Value	Description
algorithmFPType	double	The floating-point type that the algorithm uses for intermediate computations. Can be float or double.
pmethod	defaultDense	The computation method used by the multi-class classifier. The only prediction method supported so far is the multi-class classifier proposed by Ting-Fan Wu et al.
tmethod	training::oneAgainstOne	The computation method that was used to train the multi-class classifier model.
prediction	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	Not applicable	The number of classes, a required parameter.
maxIterations	100	The maximal number of iterations for the algorithm.
accuracyThreshold	1.0e-12	The prediction accuracy.

C++:

Java*: