Scalable Semi-Supervised Aggregation of Classifiers
Akshay Balsubramani ⋅ Yoav Freund
2015 Poster
Abstract
We present and empirically evaluate an efficient algorithm that learns to aggregate the predictions of an ensemble of binary classifiers. The algorithm uses the structure of the ensemble predictions on unlabeled data to yield significant performance improvements. It does this without making assumptions on the structure or origin of the ensemble, without parameters, and as scalably as linear learning. We empirically demonstrate these performance gains with random forests.
Chat is not available.
Successful Page Load