Batch Processing

BrownBoost 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 BrownBoost 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 BrownBoost classifier. The only training method supported so far is the Y. Freund's method.

nClasses

2

The number of classes.

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.

Prediction

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

float

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.

nClasses

2

The number of classes.

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.

For more complete information about compiler optimizations, see our Optimization Notice.
Select sticky button color: 
Orange (only for download buttons)