Package jsat.classifiers.boosting

Class Summary

Class	Description
AdaBoostM1	Implementation of Experiments with a New Boosting Algorithm, by Yoav Freund&Robert E.
ArcX4	Arc-x4 is a ensemble-classifier that performs re-weighting of the data points based on the total number of errors that have occurred for the data point.
Bagging	An implementation of Bootstrap Aggregating, as described by LEO BREIMAN in "Bagging Predictors".
EmphasisBoost	Emphasis Boost is a generalization of the Real AdaBoost algorithm, expanding the update term and providing the λ term to control the trade off.
LogitBoost	An implementation of the original 2 class LogitBoost algorithm.
LogitBoostPL	An extension to the original LogitBoost algorithm for parallel training.
ModestAdaBoost	Modest Ada Boost is a generalization of Discrete Ada Boost that attempts to reduce the generalization error and avoid over-fitting.
SAMME	This is an implementation of the Multi-Class AdaBoost method SAMME (Stagewise Additive Modeling using a Multi-Class Exponential loss function), presented in Multi-class AdaBoost by Ji Zhu, Saharon Rosset, Hui Zou,&Trevor Hasstie This algorithm reduces to AdaBoostM1 for binary classification problems.
Stacking	This provides an implementation of the Stacking ensemble method.
UpdatableStacking	This provides an implementation of the Stacking ensemble method meant for Updatable models.
Wagging	Wagging is a meta-classifier that is related to Bagging.
WaggingNormal	Wagging using the Normal distribution.