Items where Author is "Large, James"

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Published

Middlehurst, Matthew, Large, James, Flynn, Michael, Lines, Jason, Bostrom, Aaron and Bagnall, Anthony (2021) HIVE-COTE 2.0: a new meta ensemble for time series classification. Machine Learning, 110. 3211–3243. ISSN 0885-6125

Middlehurst, Matthew, Large, James, Cawley, Gavin and Bagnall, Anthony (2021) The Temporal Dictionary Ensemble (TDE) Classifier for Time Series Classification. In: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2020-09-14 - 2020-09-18.

Bagnall, Tony, Flynn, Michael, Large, James and Middlehurst, Matthew (2020) On the Usage and Performance of the Hierarchical Vote Collective of Transformation-Based Ensembles Version 1.0 (HIVE-COTE v1.0). In: Lecture Notes in Computer Science. Springer, pp. 3-18. ISBN 978-3-030-65741-3

Large, James, Lines, Jason and Bagnall, Anthony (2019) A probabilistic classifier ensemble weighting scheme based on cross-validated accuracy estimates. Data Mining and Knowledge Discovery, 33 (6). pp. 1674-1709. ISSN 1384-5810

Large, James, Bagnall, Anthony, Malinowski, Simon and Tavenard, Romain (2019) On time series classification with dictionary-based classifiers. Intelligent Data Analysis, 23 (5). pp. 1073-1089. ISSN 1088-467X

Large, James, Southam, Paul and Bagnall, Anthony (2019) Can Automated Smoothing Significantly Improve Benchmark Time Series Classification Algorithms? In: International Conference on Hybrid Artificial Intelligence Systems. Lecture Notes in Computer Science. Springer, pp. 50-60. ISBN 978-3-030-29858-6

Flynn, Michael, Large, James and Bagnall, Anthony (2019) The Contract Random Interval Spectral Ensemble (c-RISE): The Effect of Contracting a Classifier on Accuracy. In: International Conference on Hybrid Artificial Intelligence Systems. Lecture Notes in Computer Science. Springer, pp. 381-392. ISBN 978-3-030-29858-6

Large, James and Bagnall, Anthony (2019) Mixing hetero- and homogeneous models in weighted ensembles. In: 20th International Conference on Intelligent Data Engineering and Automated Learning. Lecture Notes in Computer Science. Springer, pp. 129-136. ISBN 978-3-030-33606-6

Large, James, Kemsley, E Kate, Wellner, Nikolaus, Goodall, Ian and Bagnall, Anthony (2018) Detecting Forged Alcohol Non-invasively Through Vibrational Spectroscopy and Machine Learning. In: PAKDD 2018: Advances in Knowledge Discovery and Data Mining. Springer, AUS, pp. 298-309. ISBN 978-3-319-93033-6

Bagnall, Anthony, Lines, Jason, Bostrom, Aaron, Large, James and Keogh, Eamonn (2017) The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Mining and Knowledge Discovery, 31 (3). 606–660. ISSN 1384-5810

This list was generated on Tue Jul 5 15:11:36 2022 UTC.