James Large
Orcid: 0000-0002-2357-3798
According to our database1,
James Large
authored at least 21 papers
between 2016 and 2021.
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Bibliography
2021
Mach. Learn., 2021
The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances.
Data Min. Knowl. Discov., 2021
2020
A tale of two toolkits, report the third: on the usage and performance of HIVE-COTE v1.0.
CoRR, 2020
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2020
On the Usage and Performance of the Hierarchical Vote Collective of Transformation-Based Ensembles Version 1.0 (HIVE-COTE v1.0).
Proceedings of the Advanced Analytics and Learning on Temporal Data, 2020
Proceedings of the 2020 IEEE International Conference on Big Data (IEEE BigData 2020), 2020
2019
Intell. Data Anal., 2019
A probabilistic classifier ensemble weighting scheme based on cross-validated accuracy estimates.
Data Min. Knowl. Discov., 2019
A tale of two toolkits, report the second: bake off redux. Chapter 1. dictionary based classifiers.
CoRR, 2019
Proceedings of the Intelligent Data Engineering and Automated Learning - IDEAL 2019, 2019
Can Automated Smoothing Significantly Improve Benchmark Time Series Classification Algorithms?
Proceedings of the Hybrid Artificial Intelligent Systems - 14th International Conference, 2019
The Contract Random Interval Spectral Ensemble (c-RISE): The Effect of Contracting a Classifier on Accuracy.
Proceedings of the Hybrid Artificial Intelligent Systems - 14th International Conference, 2019
2018
From BOP to BOSS and Beyond: Time Series Classification with Dictionary Based Classifiers.
CoRR, 2018
CoRR, 2018
Detecting Forged Alcohol Non-invasively Through Vibrational Spectroscopy and Machine Learning.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2018
2017
The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances.
Data Min. Knowl. Discov., 2017
The Heterogeneous Ensembles of Standard Classification Algorithms (HESCA): the Whole is Greater than the Sum of its Parts.
CoRR, 2017
Simulated Data Experiments for Time Series Classification Part 1: Accuracy Comparison with Default Settings.
CoRR, 2017
2016
The Great Time Series Classification Bake Off: An Experimental Evaluation of Recently Proposed Algorithms. Extended Version.
CoRR, 2016