Developer Guide

Contents

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
DEPRECATED:
Pointer to an object of the weak learner training class
USE INSTEAD:
Pointer to an object of the classification stump training class
DEPRECATED:
Pointer to the training algorithm of the weak learner. By default, a stump weak learner is used.
USE INSTEAD:
Pointer to the classifier training algorithm. Be default, a classification stump with gini split criterion is used.
weakLearnerPrediction
DEPRECATED:
Pointer to an object of the weak learner prediction class
USE INSTEAD:
Pointer to an object of the classification stump prediction class
DEPRECATED:
Pointer to the prediction algorithm of the weak learner. By default, a stump weak learner is used.
USE INSTEAD:
Pointer to the classifier prediction algorithm. Be default, a classification stump with gini split criterion 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.
newtonRaphsonMax
Iterations
100
The maximal number of Newton-Raphson iterations in the algorithm.
degenerateCases
Threshold
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
DEPRECATED:
Pointer to an object of the weak learner prediction class
USE INSTEAD:
Pointer to an object of the classification stump prediction class
DEPRECATED:
Pointer to the prediction algorithm of the weak learner. By default, a stump weak learner is used.
USE INSTEAD:
Pointer to the classifier prediction algorithm. Be default, a classification stump with gini split criterion is used.
accuracyThreshold
0.01
BrownBoost training accuracy
ε
.

Product and Performance Information

1

Intel's compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. Please refer to the applicable product User and Reference Guides for more information regarding the specific instruction sets covered by this notice.

Notice revision #20110804