Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/456370
Title: Pruning training sets for learning of object categories
Authors: Angelova, Anelia;Abu-Mostafa, Yaser;Perona, Pietro
subject: Science & Technology
Year: 2008
Abstract: 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, called 'data pruning' and demonstrate its success on learning of human faces. It is not assumed that the data or the noise can be modeled or that additional training examples are available. Our experiments show that data pruning can improve on generalization performance for algorithms with various robustness to noise. It outperforms methods with regularization properties and is superior to commonly applied aggregation methods, such as bagging.
Description: 

URI: http://localhost/handle/Hannan/366962
http://localhost/handle/Hannan/456370
Appears in Collections:2002-2008

Files in This Item:
File SizeFormat 
AL506835.pdf506.96 kBAdobe PDF
Title: Pruning training sets for learning of object categories
Authors: Angelova, Anelia;Abu-Mostafa, Yaser;Perona, Pietro
subject: Science & Technology
Year: 2008
Abstract: 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, called 'data pruning' and demonstrate its success on learning of human faces. It is not assumed that the data or the noise can be modeled or that additional training examples are available. Our experiments show that data pruning can improve on generalization performance for algorithms with various robustness to noise. It outperforms methods with regularization properties and is superior to commonly applied aggregation methods, such as bagging.
Description: 

URI: http://localhost/handle/Hannan/366962
http://localhost/handle/Hannan/456370
Appears in Collections:2002-2008

Files in This Item:
File SizeFormat 
AL506835.pdf506.96 kBAdobe PDF
Title: Pruning training sets for learning of object categories
Authors: Angelova, Anelia;Abu-Mostafa, Yaser;Perona, Pietro
subject: Science & Technology
Year: 2008
Abstract: 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, called 'data pruning' and demonstrate its success on learning of human faces. It is not assumed that the data or the noise can be modeled or that additional training examples are available. Our experiments show that data pruning can improve on generalization performance for algorithms with various robustness to noise. It outperforms methods with regularization properties and is superior to commonly applied aggregation methods, such as bagging.
Description: 

URI: http://localhost/handle/Hannan/366962
http://localhost/handle/Hannan/456370
Appears in Collections:2002-2008

Files in This Item:
File SizeFormat 
AL506835.pdf506.96 kBAdobe PDF