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Inferring "biological relevance" from "model relevance" when modeling technique has an influence on the features in the model is then questionable.
Model predictions obtained by finite element simulations compared very well to the experimental data and therefore the model relevance for computer assisted application design was assessed.
We anticipate that further research will explore this issue of process complexity versus model complexity, model relevance, and model uncertainty, which can be synthesized as a "modeling trilemma" (Muller et al. 2010).
In order to address the model "relevance trilemma" – balancing complexity, uncertainty and sensitivity, a global uncertainty and sensitivity analysis (GUSA) was conducted on these five levels of algorithm complexity.
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A fuzzy approach [15] was also used for modeling relevance feedback tasks.
This study presented promising approaches such as the concept of local similarity patterns (LSP) and the use of continuous positive and negative weights modeling relevance and undesirability of visual features.
Numerous authors have discussed the potential of BDA for IS research, for example, Shimueli and Koppius [8] described six roles for predictive analytics: generating new theory, develop measurements, comparison of competing theories, improvement of existing models, relevance assessment, and assessment of the predictability of empirical phenomena.
In the language modeling framework this has been modeled as relevance models.
The implication is that Google has discovered a mathematical model of relevance, a way of measuring some objective criteria that allows a computer to score and compare the relevance of different web-pages.
Such a model has relevance to some important engineering applications.
From this model, the relevance of in vitro electrophysiological studies to clinical plasma concentrations can be approached.
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