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James Large
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The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances
A Bagnall, J Lines, A Bostrom, J Large, E Keogh
Data mining and knowledge discovery 31, 606-660, 2017
16842017
The UEA multivariate time series classification archive, 2018
A Bagnall, HA Dau, J Lines, M Flynn, J Large, A Bostrom, P Southam, ...
arXiv preprint arXiv:1811.00075, 2018
3872018
The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances
AP Ruiz, M Flynn, J Large, M Middlehurst, A Bagnall
Data Mining and Knowledge Discovery 35 (2), 401-449, 2021
3522021
HIVE-COTE 2.0: a new meta ensemble for time series classification
M Middlehurst, J Large, M Flynn, J Lines, A Bostrom, A Bagnall
Machine Learning 110 (11), 3211-3243, 2021
1812021
The canonical interval forest (CIF) classifier for time series classification
M Middlehurst, J Large, A Bagnall
2020 IEEE international conference on big data (big data), 188-195, 2020
1072020
The temporal dictionary ensemble (TDE) classifier for time series classification
M Middlehurst, J Large, G Cawley, A Bagnall
Machine Learning and Knowledge Discovery in Databases: European Conference …, 2021
832021
A probabilistic classifier ensemble weighting scheme based on cross-validated accuracy estimates
J Large, J Lines, A Bagnall
Data mining and knowledge discovery 33 (6), 1674-1709, 2019
762019
On time series classification with dictionary-based classifiers
J Large, A Bagnall, S Malinowski, R Tavenard
Intelligent Data Analysis 23 (5), 1073-1089, 2019
642019
On the usage and performance of the hierarchical vote collective of transformation-based ensembles version 1.0 (hive-cote v1. 0)
A Bagnall, M Flynn, J Large, J Lines, M Middlehurst
Advanced Analytics and Learning on Temporal Data: 5th ECML PKDD Workshop …, 2020
612020
The contract random interval spectral ensemble (c-RISE): the effect of contracting a classifier on accuracy
M Flynn, J Large, T Bagnall
Hybrid Artificial Intelligent Systems: 14th International Conference, HAIS …, 2019
552019
Is rotation forest the best classifier for problems with continuous features?
A Bagnall, M Flynn, J Large, J Line, A Bostrom, G Cawley
arXiv preprint arXiv:1809.06705, 2018
452018
A tale of two toolkits, report the third: on the usage and performance of HIVE-COTE v1. 0
A Bagnall, M Flynn, J Large, J Lines, M Middlehurst
arXiv preprint arXiv:2004.06069, 2020
262020
The heterogeneous ensembles of standard classification algorithms (HESCA): the whole is greater than the sum of its parts
J Large, J Lines, A Bagnall
arXiv preprint arXiv:1710.09220, 2017
222017
Simulated data experiments for time series classification Part 1: accuracy comparison with default settings
A Bagnall, A Bostrom, J Large, J Lines
arXiv preprint arXiv:1703.09480, 2017
212017
Detecting forged alcohol non-invasively through vibrational spectroscopy and machine learning
J Large, EK Kemsley, N Wellner, I Goodall, A Bagnall
Advances in Knowledge Discovery and Data Mining: 22nd Pacific-Asia …, 2018
202018
From BOP to BOSS and beyond: time series classification with dictionary based classifiers
J Large, A Bagnall, S Malinowski, R Tavenard
arXiv preprint arXiv:1809.06751, 2018
142018
Can automated smoothing significantly improve benchmark time series classification algorithms?
J Large, P Southam, A Bagnall
Hybrid Artificial Intelligent Systems: 14th International Conference, HAIS …, 2019
52019
A tale of two toolkits, report the second: bake off redux. chapter 1. dictionary based classifiers
A Bagnall, J Large, M Middlehurst
arXiv preprint arXiv:1911.12008, 2019
22019
Heterogeneous ensembles and time series classification techniques for the non-invasive authentication of spirits
J Large
University of East Anglia, 2022
2022
Detecting Electric Devices in 3D Images of Bags
A Bagnall, P Southam, J Large, R Harvey
arXiv preprint arXiv:2005.02163, 2020
2020
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