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We evaluated our method on two public medical imaging datasets and it showed improved retrieval accuracy and efficiency.
The improved retrieval flexibility requires immense computational resources, which can be satisfied by utilisation of powerful parallel architectures.
By integrating the semantic evidence of relevance, the lowly ranked relevant documents are promoted in the ranking list which leads to improved retrieval performance.
The modified OMI-TOC NN algorithm exhibited a considerably improved retrieval accuracy, in terms of RMSE, over the whole validation set.
Therefore, in this article, we concentrate on obtaining a maximal discrimination between the features of different database classes, which should in turn result in improved retrieval performance.
Results demonstrate that the form was successful at eliciting more information from users and that this additional information significantly improved retrieval performance.
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The use of truncation may further improve retrieval (e.g. ventric* retrieves on ventricular, ventricle, and ventricles).
The information index is built to improve retrieval speed.
Many caching algorithms have been proposed to improve retrieval performance.
It is also possible that a more rigorous probabilistic framework could further improve retrieval accuracy.
The experimental results demonstrate that the proposed method can improve retrieval performance greatly.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com