Towards context-aware similarity metrics (invited paper)
| dc.contributor.author | Morent, Dominik | |
| dc.contributor.author | Patterson, David | deu |
| dc.contributor.author | Berthold, Michael R. | |
| dc.date.accessioned | 2013-08-23T12:15:23Z | deu |
| dc.date.available | 2013-08-23T12:15:23Z | deu |
| dc.date.issued | 2005 | |
| dc.description.abstract | Comparing molecular structures is often done based on relatively high-dimensional, binary fingerprints that represent absence or presence of certain structural elements. The then commonly used Tanimoto distance considers all those bits similar, although some of the underlying structural elements may be quite closely related. We propose a weighting scheme for such related bits which softens the influence of differences in bit patterns that represent similar contexts. We demonstrate the usefulness of this context aware similarity metric using an automated bit-weighting mechanism and a set of similarity weights based on Topomer shape similarity between the structural elements encoded in the finger print. | eng |
| dc.description.version | published | |
| dc.identifier.citation | Proceedings of 2005 International Conference on Machine Learning and Cybernetics : August 18 - 21, 2005, Ramada Hotel, Guangzhou, China; Vol. 9 / sponsored by IEEE Systems, Man and Cybernetics Technical Committee on Cybernetics ... - Piscataway, NJ : IEEE Operations Center, 2005. - S. 5596-5598. - ISBN 0-7803-9091-1 | deu |
| dc.identifier.doi | 10.1109/ICMLC.2005.1527933 | deu |
| dc.identifier.uri | http://kops.uni-konstanz.de/handle/123456789/24304 | |
| dc.language.iso | eng | deu |
| dc.legacy.dateIssued | 2013-08-23 | deu |
| dc.rights | terms-of-use | deu |
| dc.rights.uri | https://rightsstatements.org/page/InC/1.0/ | deu |
| dc.subject.ddc | 004 | deu |
| dc.title | Towards context-aware similarity metrics (invited paper) | eng |
| dc.type | INPROCEEDINGS | deu |
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| kops.citation.bibtex | @inproceedings{Morent2005Towar-24304,
year={2005},
doi={10.1109/ICMLC.2005.1527933},
title={Towards context-aware similarity metrics (invited paper)},
isbn={0-7803-9091-1},
publisher={IEEE},
booktitle={2005 International Conference on Machine Learning and Cybernetics},
pages={5596--5598 Vol. 9},
author={Morent, Dominik and Patterson, David and Berthold, Michael R.}
} | |
| kops.citation.iso690 | MORENT, Dominik, David PATTERSON, Michael R. BERTHOLD, 2005. Towards context-aware similarity metrics (invited paper). Proceedings of 2005 International Conference on Machine Learning and Cybernetics. Guangzhou, China, 21. Aug. 2005 - 21. Aug. 2005. In: 2005 International Conference on Machine Learning and Cybernetics. IEEE, 2005, pp. 5596-5598 Vol. 9. ISBN 0-7803-9091-1. Available under: doi: 10.1109/ICMLC.2005.1527933 | deu |
| kops.citation.iso690 | MORENT, Dominik, David PATTERSON, Michael R. BERTHOLD, 2005. Towards context-aware similarity metrics (invited paper). Proceedings of 2005 International Conference on Machine Learning and Cybernetics. Guangzhou, China, Aug 21, 2005 - Aug 21, 2005. In: 2005 International Conference on Machine Learning and Cybernetics. IEEE, 2005, pp. 5596-5598 Vol. 9. ISBN 0-7803-9091-1. Available under: doi: 10.1109/ICMLC.2005.1527933 | eng |
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| kops.date.conferenceEnd | 2005-08-21 | |
| kops.date.conferenceStart | 2005-08-21 | |
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| kops.identifier.nbn | urn:nbn:de:bsz:352-243041 | deu |
| kops.location.conference | Guangzhou, China | |
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| kops.sourcefield.plain | 2005 International Conference on Machine Learning and Cybernetics. IEEE, 2005, pp. 5596-5598 Vol. 9. ISBN 0-7803-9091-1. Available under: doi: 10.1109/ICMLC.2005.1527933 | deu |
| kops.sourcefield.plain | 2005 International Conference on Machine Learning and Cybernetics. IEEE, 2005, pp. 5596-5598 Vol. 9. ISBN 0-7803-9091-1. Available under: doi: 10.1109/ICMLC.2005.1527933 | eng |
| kops.submitter.email | christoph.petzmann@uni-konstanz.de | deu |
| kops.title.conference | Proceedings of 2005 International Conference on Machine Learning and Cybernetics | |
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| source.identifier.isbn | 0-7803-9091-1 | |
| source.publisher | IEEE | |
| source.title | 2005 International Conference on Machine Learning and Cybernetics |
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