Zobrazeno 1 - 10
of 14
pro vyhledávání: '"James Large"'
Autor:
James Large, Apostolos Pesyridis
Publikováno v:
Aerospace, Vol 6, Iss 5, p 55 (2019)
In this study, the on-going research into the improvement of micro-gas turbine propulsion system performance and the suitability for its application as propulsion systems for small tactical UAVs (
Externí odkaz:
https://doaj.org/article/1536acf347b84dcda266b54bdc65dbb0
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
Publikováno v:
Data Mining and Knowledge Discovery
Time Series Classification (TSC) involves building predictive models for a discrete target variable from ordered, real valued, attributes. Over recent years, a new set of TSC algorithms have been developed which have made significant improvement over
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::325375723dfae7b375e595a157209760
https://doi.org/10.1007/s10618-020-00727-3
https://doi.org/10.1007/s10618-020-00727-3
Publikováno v:
Intelligent Data Analysis. 23:1073-1089
A family of algorithms for time series classification (TSC) involve running a sliding window across each series, discretising the window to form a word, forming a histogram of word counts over the dictionary, then constructing a classifier on the his
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::68c319639526a7e3b6bd16694fba0b95
https://doi.org/10.3233/ida-184333
https://doi.org/10.3233/ida-184333
Publikováno v:
Data Mining and Knowledge Discovery
Our hypothesis is that building ensembles of small sets of strong classifiers constructed with different learning algorithms is, on average, the best approach to classification for real world problems. We propose a simple mechanism for building small
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e201f95a3f0f061601efa337511d61a5
https://doi.org/10.1007/s10618-019-00638-y
https://doi.org/10.1007/s10618-019-00638-y
Publikováno v:
IEEE BigData
Time series classification (TSC) is home to a number of algorithm groups that utilise different kinds of discriminatory patterns. One of these groups describes classifiers that predict using phase dependant intervals. The time series forest (TSF) cla
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aa4e45e1a2302ee77091fc3471bd484a
https://ueaeprints.uea.ac.uk/id/eprint/79575/
https://ueaeprints.uea.ac.uk/id/eprint/79575/
Publikováno v:
Machine Learning and Knowledge Discovery in Databases ISBN: 9783030676575
Using bag of words representations of time series is a popular approach to time series classification. These algorithms involve approximating and discretising windows over a series to form words, then forming a count of words over a given dictionary.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7e739e29c894042e00712be86498515d
https://doi.org/10.1007/978-3-030-67658-2_38
https://doi.org/10.1007/978-3-030-67658-2_38
Publikováno v:
Advanced Analytics and Learning on Temporal Data ISBN: 9783030657413
AALTD@PKDD/ECML
AALTD@PKDD/ECML
The Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) is a heterogeneous meta ensemble for time series classification. Since it was first proposed in 2016, the algorithm has undergone some minor changes and there is now a con
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f4c8c0c22a4e2634443fc5512a820171
https://doi.org/10.1007/978-3-030-65742-0_1
https://doi.org/10.1007/978-3-030-65742-0_1
Autor:
Anthony J. Bagnall, James Large
Publikováno v:
Intelligent Data Engineering and Automated Learning – IDEAL 2019 ISBN: 9783030336066
IDEAL (1)
IDEAL (1)
The effectiveness of ensembling for improving classification performance is well documented. Broadly speaking, ensemble design can be expressed as a spectrum where at one end a set of heterogeneous classifiers model the same data, and at the other ho
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::28d0b5767cbe88431ee93a60c8724b0a
https://ueaeprints.uea.ac.uk/id/eprint/72158/
https://ueaeprints.uea.ac.uk/id/eprint/72158/
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030298586
HAIS
HAIS
tl;dr: no, it cannot, at least not on average on the standard archive problems. We assess whether using six smoothing algorithms (moving average, exponential smoothing, Gaussian filter, Savitzky-Golay filter, Fourier approximation and a recursive med
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f05dd5cc0ca5327c4e33d640001ed1ae
https://doi.org/10.1007/978-3-030-29859-3_5
https://doi.org/10.1007/978-3-030-29859-3_5