Uses of Package
jsat.classifiers.boosting

Packages that use jsat.classifiers.boosting

Package	Description
jsat.classifiers.boosting

Classes in jsat.classifiers.boosting used by jsat.classifiers.boosting

Class and Description
AdaBoostM1 Implementation of Experiments with a New Boosting Algorithm, by Yoav Freund&Robert E.
AdaBoostM1PL An extension to the original AdaBoostM1 algorithm for parallel training.
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.