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Pruning Training Sets for Learning of Object Categories Anelia Angelova

Summary: Pruning Training Sets for Learning of Object Categories
Anelia Angelova§
Yaser Abu-Mostafa
Pietro Perona
Computer Science Department
Electrical Engineering Department
California Institute of Technology, Pasadena, CA 91125
Training datasets for learning of object categories are
often contaminated or imperfect. We explore an approach
to automatically identify examples that are noisy or trouble-
some 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 train-
ing 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, called 'data pruning', and demonstrate its suc-
cess on learning of human faces. It is not assumed that the


Source: Abu-Mostafa, Yaser S. - Department of Mechanical Engineering & Computer Science Department, California Institute of Technology


Collections: Computer Technologies and Information Sciences