An innovative kernel-based recursive time-series learning algorithm with applications to improvements of beehive management practices

Jack Penm

International Journal of Innovation and Learning, 2008, vol. 5, issue 2, 155-169

Abstract: 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 proportions of other foraging bees, such as pollen foragers. This suggests that we may be able to predict the optimal conditions at any time to maximise the honey yield of colonies.

Keywords: beehive management; innovation; time-series learning algorithms; subset vector autoregressive modelling; honey bees; honey bee foragers; nectar foraging; pollen foraging; honey yield. (search for similar items in EconPapers)
Date: 2008
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=16762 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijilea:v:5:y:2008:i:2:p:155-169

Access Statistics for this article

More articles in International Journal of Innovation and Learning from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().