Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Aaron Bostrom"'
Publikováno v:
ACM Transactions on Knowledge Discovery from Data. 17:1-28
Time series classification has become an interesting field of research, thanks to the extensive studies conducted in the past two decades. Time series may have missing data, which may affect both the representation and also modeling of time series. T
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9d238a3f94d0fe3c6f8f83e399c9ae42
https://doi.org/10.1145/3551643
https://doi.org/10.1145/3551643
Autor:
Aaron Bostrom, Anthony J. Bagnall, Michael Flynn, Jason Lines, James Large, Matthew Middlehurst
Publikováno v:
Machine Learning. 110:3211-3243
The Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) is a heterogeneous meta ensemble for time series classification. HIVE-COTE forms its ensemble from classifiers of multiple domains, including phase-independent shapelets,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9270b64826150d77894f85105a9b6a74
https://doi.org/10.1007/s10994-021-06057-9
https://doi.org/10.1007/s10994-021-06057-9
Autor:
Aaron Bostrom, Danny Websdale, Joshua Ball, Joshua Colmer, Rene Benjamins, Qiaojun Lou, Gema Flores Andaluz, Gagan Shiralagi, Ji Zhou, Daniel Reynolds, Steven Penfield, Wei Lu, Carmel M. O’Neill, Thomas Le Cornu, Jim Renema, Rachel Wells
Publikováno v:
New Phytologist. 228:778-793
Efficient seed germination and establishment are important traits for field and glasshouse crops. Large-scale germination experiments are laborious and prone to observer errors, leading to the necessity for automated methods. We experimented with fiv
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8a392b674accfbfe7f9034d134c7aff6
https://doi.org/10.1111/nph.16736
https://doi.org/10.1111/nph.16736
Autor:
Joshua Ball, Tao Cheng, Ji Zhou, Alan Bauer, Sergio Moreno Rojas, Christopher Applegate, Aaron Bostrom, Jacob Kirwan, Stephen D. Laycock
Publikováno v:
Horticulture Research
Horticulture Research, Vol 6, Iss 1, Pp 1-12 (2019)
Horticulture Research, Vol 6, Iss 1, Pp 1-12 (2019)
Aerial imagery is regularly used by crop researchers, growers and farmers to monitor crops during the growing season. To extract meaningful information from large-scale aerial images collected from the field, high-throughput phenotypic analysis solut
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a298ab45f8ce621e942d4df9a3e42a0f
http://europepmc.org/articles/PMC6544649
http://europepmc.org/articles/PMC6544649
Autor:
Kirwan J, Christopher Applegate, Ji Zhou, Joshua Ball, Rojas Sm, Stephen D. Laycock, Tao Cheng, Aaron Bostrom, Bauer A
Aerial imagery is regularly used by farmers and growers to monitor crops during the growing season. To extract meaningful phenotypic information from large-scale aerial images collected regularly from the field, high-throughput analytic solutions are
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::324addc2715acd61d626dd5741122455
Autor:
Mehdi Khafif, François Tardieu, Ji Zhou, Aakash Chawade, Francesco Cellini, Koji Noshita, Daniel Reynolds, Mark Mueller-Linow, Joshua Ball, Aaron Bostrom, Argelia Lorence, Claude Welcker, Frédéric Baret
Publikováno v:
Plant Science
Plant Science, Elsevier, 2019, 282, pp.14-22. ⟨10.1016/j.plantsci.2018.06.015⟩
Plant Science (282), 14-22. (2019)
Plant Science, 2019, 282, pp.14-22. ⟨10.1016/j.plantsci.2018.06.015⟩
Plant Science, Elsevier, 2019, 282, pp.14-22. ⟨10.1016/j.plantsci.2018.06.015⟩
Plant Science (282), 14-22. (2019)
Plant Science, 2019, 282, pp.14-22. ⟨10.1016/j.plantsci.2018.06.015⟩
International audience; Progress in remote sensing and robotic technologies decreases the hardware costs of phenotyping. Here, we first review cost-effective imaging devices and environmental sensors, and present a trade-off between investment and ma
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::66794140262819fdd9e226fc35a6d341
https://hal.inrae.fr/hal-02628161/file/Reynolds-PS-2018-accepted-LEPSE_1.pdf
https://hal.inrae.fr/hal-02628161/file/Reynolds-PS-2018-accepted-LEPSE_1.pdf
Autor:
Aaron Bostrom, Anthony J. Bagnall
Publikováno v:
Transactions on Large-Scale Data-and Knowledge-Centered Systems XXXII ISBN: 9783662556078
Big Data Analytics and Knowledge Discovery ISBN: 9783319227283
DaWaK
Big Data Analytics and Knowledge Discovery ISBN: 9783319227283
DaWaK
Shapelets have recently been proposed as a new primitive for time series classification. Shapelets are subseries of series that best split the data into its classes. In the original research, shapelets were found recursively within a decision tree th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::776f1ab5553463a150644deb129c07b2
https://doi.org/10.1007/978-3-662-55608-5_2
https://doi.org/10.1007/978-3-662-55608-5_2
Publikováno v:
ICDE
Recently, two ideas have been explored that lead to more accurate algorithms for time-series classification (TSC). First, it has been shown that the simplest way to gain improvement on TSC problems is to transform into an alternative data space where
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::add612812d1a6bd04cd1dc32727c7746
https://doi.org/10.1109/icde.2016.7498418
https://doi.org/10.1109/icde.2016.7498418
Publikováno v:
Data mining and knowledge discovery, vol 31, iss 3
Data Mining and Knowledge Discovery
Data Mining and Knowledge Discovery
In the last five years there have been a large number of new time series classification algorithms proposed in the literature. These algorithms have been evaluated on subsets of the 47 data sets in the University of California, Riverside time series
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::043411ce05d7986778e4f8955a11d7e9