A generalised label noise model for classification
Learning from labelled data is becoming more and more challenging due to inherent imperfection of training labels. In this paper, we propose a new, generalised label noise model which is able to withstand the negative effect of both random noise and a wide range of non-random label noises. Empirical...
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| Main Author: | |
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| Format: | Conference Proceeding |
| Published: |
2018
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| Subjects: | |
| Online Access: | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84961807046&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/44560 |
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| Institution: | Chiang Mai University |
| Summary: | Learning from labelled data is becoming more and more challenging due to inherent imperfection of training labels. In this paper, we propose a new, generalised label noise model which is able to withstand the negative effect of both random noise and a wide range of non-random label noises. Empirical studies using three real-world datasets with inherent annotation errors demonstrate that the proposed generalised label noise model improves, in terms of classification accuracy, over existing label noise modelling approaches. |
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