2017

[1]

Probability Calibration Trees

Tim Leathart, Eibe Frank, Geoffrey Holmes, and Bernhard Pfahringer. Probability calibration trees. In Proc 9th Asian Conference on Machine Learning, Seoul, Korea, pages 145-160. Proceedings of Machine Learning Research, 2017.
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[2]

Learning Through Utility Optimization in Regression Tasks

Paula Branco, Luis Torgo, Rita P. Ribeiro, Eibe Frank, Bernhard Pfahringer, and Markus Michael Rau. Learning through utility optimization in regression tasks. In Proc 4th IEEE International Conference on Data Science and Advanced Analytics, Tokyo Japan. IEEE, 2017.
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[3]

Online estimation of discrete, continuous, and conditional joint densities using classifier chains

Michael Geilke, Andreas Karwath, Eibe Frank, and Stefan Kramer. Online estimation of discrete, continuous, and conditional joint densities using classifier chains. Data Mining and Knowledge Discovery, 2017.
[ bib | http ]
[4]

Proximity Assurances Based on Natural and Artificial Ambient Environments

Iakovos Gurulian, Konstantinos Markantonakis, Carlton Shepherd, Eibe Frank, and Raja Naeem Akram. Proximity assurances based on natural and artificial ambient environments. In Proc 10th International Conference on Information Technology and Communications Security, Bucharest, Romania, pages 83-103. Springer, 2017.
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[5]

Accelerating the XGBoost algorithm using GPU computing

Rory Mitchell and Eibe Frank. Accelerating the XGBoost algorithm using GPU computing. PeerJ Computer Science, 3(e127), 2017.
[ bib | http ]
[6]

Large-scale automatic species identification

Jeff Mo, Eibe Frank, and Varvara Vetrova. Large-scale automatic species identification. In Proc 30th Australasian Joint Conference on Artificial Intelligence. Springer, 2017.
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[7]

On the Effectiveness of Ambient Sensing for Detecting NFC Relay Attacks

Iakovos Gurulian, Carlton Shepherd, Eibe Frank, Konstantinos Markantonakis, Raja Naeem Akram, and Keith Mayes. On the effectiveness of ambient sensing for detecting NFC relay attacks. In Proc 6th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, Sydney, Australia. IEEE, 2017.
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[8]

The Applicability of Ambient Sensors as Proximity Evidence for NFC Transactions

Carlton Shepherd, Iakovos Gurulian, Konstantinos Markantonakis, Eibe Frank, Raja Naeem Akram, Emmanouil Panaousis, and Keith Mayes. The applicability of ambient sensors as proximity evidence for NFC transactions. In Proc 6th Workshop on Mobile Security Technologies (MoST), San Jose, United States. IEEE Computer Society's Technical Committee on Security and Privacy, 2017.
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[9]

WASSA-2017 Shared Task on Emotion Intensity

Saif M. Mohammad and Felipe Bravo-Marquez. WASSA-2017 shared task on emotion intensity. In Proceedings of the Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA), Copenhagen, Denmark, 2017.
[ bib | http ]
[10]

Emotion Intensities in Tweets

Saif M. Mohammad and Felipe Bravo-Marquez. Emotion intensities in tweets. In Proceedings of the sixth joint conference on lexical and computational semantics (*Sem), Vancouver, Canada, 2017.
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[11]

Extremely Fast Decision Tree Mining for Evolving Data Streams

Albert Bifet, Jiajin Zhang, Wei Fan, Cheng He, Jianfeng Zhang, Jianfeng Qian, Geoff Holmes, and Bernhard Pfahringer. Extremely fast decision tree mining for evolving data streams. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, August 13 - 17, 2017, pages 1733-1742, 2017.
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[12]

A survey on feature drift adaptation: Definition, benchmark, challenges and future directions

Jean Paul Barddal, Heitor Murilo Gomes, Fabrício Enembreck, and Bernhard Pfahringer. A survey on feature drift adaptation: Definition, benchmark, challenges and future directions. Journal of Systems and Software, 127:278-294, 2017.
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[13]

Foreword: special issue for the journal track of the 8th Asian conference on machine learning (ACML 2016)

Robert J. Durrant, Kee-Eung Kim, Geoffrey Holmes, Stephen Marsland, Masashi Sugiyama, and Zhi-Hua Zhou. Foreword: special issue for the journal track of the 8th asian conference on machine learning (ACML 2016). Machine Learning, 106(5):623-625, 2017.
[ bib | http ]
[14]

Aesthetic Local Search of Wind Farm Layouts

Michael Mayo and Maisa Daoud. Aesthetic local search of wind farm layouts. Information, 8(2):39, 2017.
[ bib | http ]
[15]

Constructing Document Vectors Using Kernel Density Estimates

Michael Mayo and Sean Goltz. Constructing document vectors using kernel density estimates. In Modeling Decisions for Artificial Intelligence - 14th International Conference, MDAI 2017, Kitakyushu, Japan, October 18-20, 2017, Proceedings, pages 183-194, 2017.
[ bib | http ]
[16]

Surrogate modeling a computational fluid dynamics-based wind turbine wake simulation using machine learning

Brett Wilson, Sarah Wakes, and Michael Mayo. Surrogate modeling a computational fluid dynamics-based wind turbine wake simulation using machine learning. In 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017, Honolulu, HI, USA, November 27 - Dec. 1, 2017, pages 1-8, 2017.
[ bib | http ]