Alistair Shilton
Alistair Shilton
Applied Artificial Intelligence Institute (AČIČ), Deakin University
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Guidelines for developing and reporting machine learning predictive models in biomedical research: a multidisciplinary view
W Luo, D Phung, T Tran, S Gupta, S Rana, C Karmakar, A Shilton, ...
Journal of medical Internet research 18 (12), e323, 2016
Detecting selective forwarding attacks in wireless sensor networks using support vector machines
S Kaplantzis, A Shilton, N Mani, YA Sekercioglu
2007 3rd International conference on intelligent sensors, sensor networks …, 2007
Incremental training of support vector machines
A Shilton, M Palaniswami, D Ralph, AC Tsoi
IEEE transactions on neural networks 16 (1), 114-131, 2005
High dimensional Bayesian optimization using dropout
C Li, S Gupta, S Rana, V Nguyen, S Venkatesh, A Shilton
arXiv preprint arXiv:1802.05400, 2018
Multi-objective Bayesian optimisation with preferences over objectives
M Abdolshah, A Shilton, S Rana, S Gupta, S Venkatesh
Advances in neural information processing systems 32, 2019
Bayesian optimization for categorical and category-specific continuous inputs
D Nguyen, S Gupta, S Rana, A Shilton, S Venkatesh
Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 5256-5263, 2020
Distributed data fusion using support vector machines
S Challa, M Palaniswami, A Shilton
Proceedings of the Fifth International Conference on Information Fusion …, 2002
DP1SVM: A dynamic planar one-class support vector machine for Internet of Things environment
A Shilton, S Rajasegarar, C Leckie, M Palaniswami
2015 International Conference on Recent Advances in Internet of Things (RIoT …, 2015
A division algebraic framework for multidimensional support vector regression
A Shilton, DTH Lai, M Palaniswami
IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 40 …, 2009
Regret bounds for transfer learning in Bayesian optimisation
A Shilton, S Gupta, S Rana, S Venkatesh
Artificial Intelligence and Statistics, 307-315, 2017
Combined multiclass classification and anomaly detection for large-scale wireless sensor networks
A Shilton, S Rajasegarar, M Palaniswami
2013 IEEE eighth international conference on intelligent sensors, sensor …, 2013
Iterative fuzzy support vector machine classification
A Shilton, DTH Lai
2007 IEEE International Fuzzy Systems Conference, 1-6, 2007
Fast supersymmetry phenomenology at the Large Hadron Collider using machine learning techniques
A Buckley, A Shilton, MJ White
Computer Physics Communications 183 (4), 960-970, 2012
Machine learning using support vector machines
M Palaniswami, A Shilton, D Ralph, BD Owen
International conference on Artificial Intelligence in Science and …, 2000
Adaptive support vector machines for regression
M Palaniswami, A Shilton
Proceedings of the 9th International Conference on Neural Information …, 2002
Exploiting strategy-space diversity for batch Bayesian optimization
S Gupta, A Shilton, S Rana, S Venkatesh
International conference on artificial intelligence and statistics, 538-547, 2018
Expected hypervolume improvement with constraints
M Abdolshah, A Shilton, S Rana, S Gupta, S Venkatesh
2018 24th International Conference on Pattern Recognition (ICPR), 3238-3243, 2018
Regression models for estimating gait parameters using inertial sensors
BK Santhiranayagam, D Lai, A Shilton, R Begg, M Palaniswami
2011 Seventh International Conference on Intelligent Sensors, Sensor …, 2011
Automatic detection of different walking conditions using inertial sensor data
BK Santhiranayagam, DTH Lai, C Jiang, A Shilton, R Begg
The 2012 international joint conference on neural networks (IJCNN), 1-6, 2012
Distributed training of multiclass conic-segmentation support vector machines on communication constrained networks
S Rajasegarar, A Shilton, C Leckie, R Kotagiri, M Palaniswami
2010 Sixth International Conference on Intelligent Sensors, Sensor Networks …, 2010
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