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Jain, Brijnesh J., Schultz, David (2018) Asymmetric learning vector quantization for efficient nearest neighbor classification in dynamic time warping spaces. Pattern Recognition, 76. 349-366 doi:10.1016/j.patcog.2017.10.029

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Reference TypeJournal (article/letter/editorial)
TitleAsymmetric learning vector quantization for efficient nearest neighbor classification in dynamic time warping spaces
JournalPattern Recognition
AuthorsJain, Brijnesh J.Author
Schultz, DavidAuthor
Year2018 (April)Volume76
PublisherElsevier BV
DOIdoi:10.1016/j.patcog.2017.10.029Search in ResearchGate
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Mindat Ref. ID9367248Long-form Identifiermindat:1:5:9367248:7
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Full ReferenceJain, Brijnesh J., Schultz, David (2018) Asymmetric learning vector quantization for efficient nearest neighbor classification in dynamic time warping spaces. Pattern Recognition, 76. 349-366 doi:10.1016/j.patcog.2017.10.029
Plain TextJain, Brijnesh J., Schultz, David (2018) Asymmetric learning vector quantization for efficient nearest neighbor classification in dynamic time warping spaces. Pattern Recognition, 76. 349-366 doi:10.1016/j.patcog.2017.10.029
In(2018) Pattern Recognition Vol. 76. Elsevier BV

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Zhao, Jiaping, Itti, Laurent (2018) shapeDTW: Shape Dynamic Time Warping. Pattern Recognition, 74. 171-184 doi:10.1016/j.patcog.2017.09.020
Jain, Brijnesh, Schultz, David (2020) Sufficient conditions for the existence of a sample mean of time series under dynamic time warping. Annals of Mathematics and Artificial Intelligence, 88 (4) 313-346 doi:10.1007/s10472-019-09682-2
Jeong, Young-Seon, Jeong, Myong K., Omitaomu, Olufemi A. (2011) Weighted dynamic time warping for time series classification. Pattern Recognition, 44 (9). 2231-2240 doi:10.1016/j.patcog.2010.09.022
 
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