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DC Field | Value | Language |
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dc.contributor.author | Ildar Batyrshin | en_US |
dc.contributor.author | Thongchai Dumrongpokaphan | en_US |
dc.contributor.author | Vladik Kreinovich | en_US |
dc.contributor.author | Olga Kosheleva | en_US |
dc.date.accessioned | 2018-09-05T02:58:24Z | - |
dc.date.available | 2018-09-05T02:58:24Z | - |
dc.date.issued | 2016-01-01 | en_US |
dc.identifier.issn | 16113349 | en_US |
dc.identifier.issn | 03029743 | en_US |
dc.identifier.other | 2-s2.0-85006021701 | en_US |
dc.identifier.other | 10.1007/978-3-319-49046-5_39 | en_US |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85006021701&origin=inward | en_US |
dc.identifier.uri | http://cmuir.cmu.ac.th/jspui/handle/6653943832/55607 | - |
dc.description.abstract | © Springer International Publishing AG 2016. When practitioners analyze the similarity between time series, they often use correlation to gauge this similarity. Sometimes this works, but sometimes, this leads to counter-intuitive results, in which case other similarity measures are more appropriate. An important question is how to select an appropriate similarity measures. In this paper, we show, on simple examples, that the use of natural symmetries – scaling and shift – can help with such a selection. | en_US |
dc.subject | Computer Science | en_US |
dc.subject | Mathematics | en_US |
dc.title | How to select an appropriate similarity measure: Towards a symmetry-based approach | en_US |
dc.type | Book Series | en_US |
article.title.sourcetitle | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | en_US |
article.volume | 9978 LNAI | en_US |
article.stream.affiliations | Instituto Politecnico Nacional | en_US |
article.stream.affiliations | Chiang Mai University | en_US |
article.stream.affiliations | University of Texas at El Paso | en_US |
Appears in Collections: | CMUL: Journal Articles |
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