| EPSRC Reference: |
GR/R84801/01 |
| Title: |
Learning Classifiers from Sloppily Labelled Data |
| Principal Investigator: |
Lawrence, Professor ND |
| Other Investigators: |
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| Department: |
Computer Science |
| Organisation: |
University of Sheffield |
| Scheme: |
Fast Stream |
| Starts: |
22 October 2002 |
Ends: |
21 October 2005 |
Value (£): |
65,462
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| EPSRC Research Topic Classifications: |
| Artificial Intelligence |
Image & Vision Computing |
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| EPSRC Industrial Sector Classifications: |
| No relevance to Underpinning Sectors |
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Summary on Grant Application Form |
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Data noise is present in many machine learning domains, some of these are well studied, for example target noise in a regression problem, but others such as label noise in a classification scenario have received less attention. In a presentation at the last International Conference of Machine, Learning, the applicants showed that it was possible to model label noise and account for the problems it causes within a classification task. They studied the performance of the algorithm in a simple image understanding task, the detection of sky in an image. In particular, they utilised their algorithm to demonstrate that it was possible to learn good classifiers when there is a large quantity of label noise. They were able to exploit this characteristic and label their sky classification data-set 'sloppily'. Sloppy labelling of the data-set is far less time consuming than accurate labelling and as a result it is far cheaper.The aim of this proposal is to demonstrate that the slippy labelling technique can be applied to more complex tasks, such as face detection, vehicle detection and others. To achieve these aims, several issures need to be addressed; amongst them are problem representation, efficiency and data-set collection.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
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| Date Materialised |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.shef.ac.uk |