Batch Processing

AdaBoost 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, an AdaBoost 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 AdaBoost classifier. The only training method supported so far is the Y. Freund's method.

weakLearnerTraining

Pointer to an object of the stump 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 stump prediction class

Pointer to the prediction algorithm of the weak learner. By default, a stump weak learner is used.

accuracyThreshold

0.01

AdaBoost training accuracy.

maxIterations

100

The maximal number of iterations for the algorithm.

Prediction

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

At the prediction stage, an AdaBoost 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 AdaBoost classifier at the prediction stage.

weakLearnerPrediction

Pointer to an object of the stump prediction class

Pointer to the prediction algorithm of the weak learner. By default, a stump weak learner is used.

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