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+Pruning training sets for learning of object categories|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/1467308/|2005|98|19|6629732990128685315|None|http://scholar.google.com/scholar?cites=6629732990128685315&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=6629732990128685315&hl=en&as_sdt=0,5|None|Training datasets for learning of object categories are often contaminated or imperfect. We explore an approach to automatically identify examples that are noisy or troublesome for learning and exclude them from the training set. The problem is relevant to learning in semi-supervised or unsupervised setting, as well as to learning when the training data is contaminated with wrongly labeled examples or when correctly labeled, but hard to learn examples, are present. We propose a fully automatic mechanism for noise cleaning …