Projects authored by francoisdavid collin.
MultiBoost is a multi-purpose boosting package implemented in C++. It is based on the multi-class/multi-task AdaBoost.MH algorithm [Schapire-Singer, 1999]. Basic base learners (stumps, trees, products, Haar filters for image processing) can be easily complemented by new data representations and the corresponding base learners, without interfering with the main boosting engine.
A new fast (sublinear in the number of instances) stump algorithm is implemented. The gain in time is proportional to the sparsity of the features (it is significant when a lot of instances take the most frequent feature value). See Section B.2 in the documentation.
A parametrized early stopping option is added in --traintest mode. We stop if the (smoothed) test error does not improve for a certain number of iterations. See Section 4.1.3 in the documentation.
- Operating System:
Mac Os X
- Data Formats:
- JMLR-MLOSS Publication:
Large Scale Learning,