An innovative kernel-based recursive time-series learning algorithm with applications to improvements of beehive management practices
In this paper, we propose an innovative kernel-based learning algorithm to sequentially estimate subset vector autoregressive models (including full-order models). To demonstrate the effectiveness of the proposed recursive algorithm, we apply this algorithm to test the direct causal relationships between the population of honeybee foragers and foraging types gathering nectar, pollen or water. We have found that under certain conditions, nectar foraging may be improved by the changes in the...[Show more]
|Collections||ANU Research Publications|
|Source:||International Journal of Innovation and Learning|
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