Real AdaBoost ensembles have exceptional capabilities for successfully solving classification problems. This characteristic comes from progressively ...
Nov 2, 2015 · In this paper, we introduce a simple modification which uses the neighborhood concept to reduce the above drawbacks.
Real AdaBoost ensembles have exceptional capabilities for successfully solving classification problems. This characteristic comes from progressively ...
Real AdaBoost ensembles have exceptional capabilities for success- fully solving classification problems. This characteristic comes from progres- sively ...
Apr 16, 2024 · Boosting. Boosting is an ensemble technique that combines multiple weak learners (models that perform slightly better than random guessing) ...
Vote-boosting is a sequential ensemble learning method in which the individual classifiers are built on different weighted versions of the training data.
Real AdaBoost ensembles have exceptional capabilities for successfully solving classification problems. This characteristic comes from progressively.
In this paper, we propose to make a fusion of the outputs of RA-we ensembles trained with different emphasis adjustments by means of a generalized voting scheme ...
Missing: Smoothed | Show results with:Smoothed
OC has been shown to be an effective method in boosting “weak” binary classifiers for multi-class learning. It employs the Error-Correcting Output Code (ECOC) ...
Figueiras-Vidal, Smoothed emphasis for boosting ensembles, in: I. Rojas, G. Joya, J. Cabestany (Eds.), Advances in · Computational Intelligence (LNCS 7902) ...