Improved Boosting Performance by Explicit Handling of Ambiguous Positive Examples

Abstract

Visual classes naturally have ambiguous examples, that are different depending on feature and classifier and are hard to disambiguate from surrounding negatives without overfitting. Boosting in particular tends to overfit to such hard and ambiguous examples, due to its flexibility and typically aggressive loss functions. We propose a two-pass learning… (More)
DOI: 10.1007/978-3-319-12610-4_2

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