An aligned subtree kernel for weighted graphs
- Submitting institution
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Queen Mary University of London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 539
- Type
- E - Conference contribution
- DOI
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- Title of conference / published proceedings
- Proceeding of Machine Learning Research
- First page
- 30
- Volume
- 37
- Issue
- -
- ISSN
- -
- Open access status
- -
- Month of publication
- July
- Year of publication
- 2015
- URL
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- Supplementary information
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- Request cross-referral to
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- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
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- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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3
- Research group(s)
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-
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper advanced the state-of-the-art of graph-based machine learning by developing a new graph kernel based on weighted subtrees. It was presented at the 2015 International Conference on Machine Learning (acceptance rate 270/1037 = 26.0% https://www.openresearch.org/wiki/ICML). The work was the result of an international collaboration with the Central University of Finance and Economics in Beijing (bailucs@cufe.edu.cn) and Xiamen University (zhihong@xmu.edu.cn). This paper motivated the organisation of the workshop on Pattern Recognition in Intelligent Financial Analysis and Risk Management (https://iapr.org/archives/icpr2018/rtf/ICPR18_ContentListWeb_1.html) and a related Pattern Recognition special issue (https://www.journals.elsevier.com/pattern-recognition/call-for-papers/graph-based-methods).
- Author contribution statement
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- Non-English
- No
- English abstract
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