IIIT Hyderabad Publications |
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Online Active Learning of Reject Option ClassifiersAuthors: kulin Shah,Naresh Manwani Conference: 34th AAAI Conference on Artificial Intelligence (AAAI-2020 2020) Location New York, USA Date: 2020-02-07 Report no: IIIT/TR/2020/4 AbstractActive learning is an important technique to reduce the number of labeled examples in supervised learning. Active learning for binary classification has been well addressed in machine learning. However, active learning of the reject option classifier remains unaddressed. In this paper, we propose novel algorithms for active learning of reject option classifiers. We develop an active learning algorithm using double ramp loss function. We provide mistake bounds for this algorithm. We also propose a new loss function called double sigmoid loss function for reject option and corresponding active learning algorithm. We offer a convergence guarantee for this algorithm. We provide extensive experimental results to show the effec- tiveness of the proposed algorithms. The proposed algorithms efficiently reduce the number of label examples required. Full paper: pdf Centre for Visual Information Technology |
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