Batch Processing

Training

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

At the training stage, a BrownBoost 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 BrownBoost classifier. The only training method supported so far is the Y. Freund's method.
weakLearnerTraining	Pointer to an object of the weak learner training class	Pointer to the training algorithm of the weak learner. By default, a stump weak learner is used.
weakLearnerPrediction	Pointer to an object of the weak learner prediction class	Pointer to the prediction algorithm of the weak learner. By default, a stump weak learner is used.
accuracyThreshold	0.01	BrownBoost training accuracy ε.
maxIterations	100	The maximal number of iterations for the BrownBoost algorithm.
newtonRaphsonAccuracyThreshold	1.0e-3	Accuracy threshold of the Newton-Raphson method used underneath the BrownBoost algorithm.
newtonRaphsonMaxIterations	100	The maximal number of Newton-Raphson iterations in the algorithm.
degenerateCasesThreshold	1.0e-2	The threshold used to avoid degenerate cases.

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

At the prediction stage, a BrownBoost 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	Performance-oriented computation method, the only method supported by the BrownBoost classifier.
weakLearnerPrediction	Pointer to an object of the weak learner prediction class	Pointer to the prediction algorithm of the weak learner. By default, a stump weak learner is used.
accuracyThreshold	0.01	BrownBoost training accuracy ε.