Abstract. Smooth boosting algorithms are variants of boosting methods which handle only smooth distributions on the data. They are proved to be noise-tolerant and can be used in th...
AdaBoost.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) method ...
A new family of boosting algorithms, denoted TaylorBoost, is proposed. It supports any combination of loss function and first or second order optimization, and includes classical...
Mohammad Saberian, Hamed Masnadi-Shirazi, Nuno Vas...
Boosting methods are known not to usually overfit training data even as the size of the generated classifiers becomes large. Schapire et al. attempted to explain this phenomenon i...
We describe a new boosting algorithm that is the first such algorithm to be both smooth and adaptive. These two features make possible performance improvements for many learning ...