Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Sparse Bayesian Learning for Efficient Visual Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Engineering Applications of Artificial Intelligence
The evidence framework applied to classification networks
Neural Computation
3D human pose from silhouettes by relevance vector regression
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
The relevance vector machine technique for channel equalization application
IEEE Transactions on Neural Networks
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A new collaborative control parameter design strategy is proposed for economic plant control process. The relevance vector machines (RVMs) and genetic algorithms (GAs) are combined to generate the optimal control index table for controllers. More specifically, the probabilistic model based on RVMs is utilised to describe the non-linear behaviours according to the experimental dataset. The evolution-based optimisation model based on GAs is used for collaborative design of the optimum control parameter combinations. A variable-rate fertilising system is presented as an application case for collaborative generation of control index table with the combined accuracy, energy saving and fertilising-consistency optimisation objectives. The experimental results show the effectiveness of the proposed hybrid approach.