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Boosting-by-Filtering

We examine boosting in the filtering framework, where the learner does not use a fixed training set but rather has access to an example oracle which can produce an unlimited number of examples from the target distribution. This setting is useful for modeling learning with datasets too large to fit into a computer, learning in memory-limited situations, or learning from an online source of examples (e.g. from a web crawler).

Code

Two implementations of FilterBoost are available here:

Documents

Joseph K. Bradley and Robert E. Schapire.
FilterBoost: Regression and Classification on Large Datasets.
Advances in Neural Information Processing Systems (NIPS), 2008.

Joseph K. Bradley.
FilterBoost: Regression and Classification on Large Datasets.
Data Analysis Project (Master's thesis equivalent) for the Machine Learning program at Carnegie Mellon University, 2009.