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The objective of this paper is to present a new Data-mining architecture that will include conventional Data-mining algorithms, feature selection methods and algorithmic fusion techniques to best exploit the set of labeled training patterns so as to improve the design of the overall classification system.
There are many options for algorithmic fusion, and the choice of algorithm does offer some freedom in determining what information is lost, but information is necessarily lost.
Performance is no worse than algorithmic fusion.
The sessions alternated between algorithmic and cognitive fusion (e.g., Day 2: cognitive fusion, Day 3: algorithmic fusion, Day 4: cognitive fusion, Day 5: algorithmic fusion).
Algorithmic fusion has been the focus of much of the research on presenting multi-spectral information.
Often, added image enhancement techniques are used to provide benefits above raw algorithmic fusion.
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Based on the existing research with algorithmic image fusion, we expected fusion would provide, at a minimum, equally efficient processing as an UCIP model.
This lack of efficiency improvement was evident with both algorithmic and cognitive fusion.
These data indicate no significant speed accuracy impact on processing efficiencies for either algorithmic or cognitive fusion.
Across the two experiments, we found strong evidence of limited capacity for both algorithmic and cognitive fusion.
Even with many experimental changes, we consistently found limited workload capacity with both algorithmic and cognitive fusion.
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