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KDD
2001
ACM
128views Data Mining» more  KDD 2001»
15 years 10 months ago
Experimental comparisons of online and batch versions of bagging and boosting
Nikunj C. Oza, Stuart J. Russell
ECCV
2010
Springer
14 years 10 months ago
MIForests: Multiple-Instance Learning with Randomized Trees
Abstract. Multiple-instance learning (MIL) allows for training classifiers from ambiguously labeled data. In computer vision, this learning paradigm has been recently used in many ...
Christian Leistner, Amir Saffari, Horst Bischof