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Furthermore, we propose two adaptations of the fuzzy pattern matching algorithm called fuzzy pattern matching with exponential function and fuzzy pattern matching multidensity.
We propose two adaptations of the model in which interventions evaluated in randomised controlled trials can be "out of control" [ 5].
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To reduce this bias and improve cross-corpus performance, we propose two novel adaptation-based approaches - (1) instance selection and (2) weight regularization.
In this work, we propose two algorithms based on adaptations of classical algorithms to multivariate geostatistical data.
Given aforementioned concerns, we propose two variants (Table 1) of the adaptation method which we further denote as the Improved Adaptation Method.
We propose two different ways to overcome this restriction, one based on adaptation ideas, and the other one adding an extra damping injection to the controller.
We propose two options.
Specifically, we propose two models.
Here, we propose two alternative strategies.
Here, we propose two potential mechanisms.
To compensate for this bias and improve cross-corpus performance, we proposed two novel adaptation-based approaches.
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