Details

Given feature vectors of size , a vector of class labels where in case of binary classification and , where is a number of classes, describes the class the feature vector belongs to, and is a weak learner algorithm, the problem is to build an AdaBoost classifier.

Training Stage

Initialize weights for
For :
Train the weak learner using weights .
Choose a confidence value , where
Update , where is a normalization factor.
Output the final hypothesis:

Initialize weights for
For :
Train the weak learner using weights .
Receive the weighed class probability estimates from weak learner:

For , set :

For , update :

where is a normalization factor, ,
Output the final hypothesis:

Given the AdaBoost classifier and feature vectors , the problem is to calculate the final class :

Given the AdaBoost classifier and feature vectors , the problem is to calculate the final class :

where is as defined above in Training Stage.

Batch Processing

Training Parameters for AdaBoost Multiclass Classifier (Batch Processing)

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 the classifier that returns labels as weak learner sammeR - uses the 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		AdaBoost training accuracy.
maxIterations		The maximal number of iterations for the algorithm.
learningRate		Multiplier for each classifier to shrink its contribution.
nClasses		The number of classes.
resultsToCompute		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

Training Output for AdaBoost Multiclass Classifier (Batch Processing)

Result ID	Result
weakLearnersErrors	A numeric table 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 Parameters for AdaBoost Multiclass Classifier (Batch Processing)

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		The number of classes.

Examples

adaboost_samme_two_class_batch.cpp
adaboost_sammer_two_class_batch.cpp
adaboost_samme_multi_class_batch.cpp
adaboost_sammer_multi_class_batch.cpp

AdaBoostSammeTwoClassBatch.java
AdaBoostSammerTwoClassBatch.java
AdaBoostSammeMultiClassBatch.java
AdaBoostSammerMultiClassBatch.java