Developer Guide

Contents

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
Available methods for computation of the AdaBoost algorithm:
  • samme
    - uses classifier that returns labels as weak learner.
  • sammeR
    - uses classifier that returns probabilities of belonging to class as weak learner.
  • defaultDense
    is equal to
    samme
    method.
weakLearnerTraining
Pointer to an object of the classification stump training class
Pointer to the training algorithm of the weak learner. By default, a classification stump weak learner is used.
weakLearnerPrediction
Pointer to an object of the classification stump prediction class
Pointer to the prediction algorithm of the weak learner. By default, a classification stump weak learner is used.
accuracyThreshold
0.01
AdaBoost training accuracy.
maxIterations
100
The maximal number of iterations for the algorithm.
learningRate
1.0
Multiplier for each classifier to shrink its contribution.
nClasses
2
The number of classes.
resultsToCompute
0
The 64-bit integer flag that specifies which extra characteristics of AdaBoost to compute. Current version of the library only provides the following option:
computeWeakLearnersErrors

Output

In addition to classifier output, AdaBoostcalculates the result described below. Pass the Result ID as a parameter to the methods that access the result of your algorithm. For more details, see Algorithms.
Result ID
Result
weakLearnersErrors
Numeric table 1 x
maxIterations
containing weak learner's classification errors computed when the
computeWeakLearnersErrors
option is on. By default, this result is an object of the
HomogenNumericTable
class, but you can define the result as an object of any class derived from
NumericTable
.

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 classification stump prediction class
Pointer to the prediction algorithm of the weak learner. By default, a classification stump weak learner is used.
nClasses
2
The number of classes.

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