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To test the discriminating power of selected measures, we built a support vector machine (SVM) classifier and evaluated its accuracy in distinguishing patients with PD from healthy subjects.
To investigate the discriminating power of the extracted measures, we built different SVM classifiers to distinguish PD patients from control subjects and trained each classifier using a different dataset of measures.
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For each cotinine/smoking measure, we built two models.
Further, although we did not expect a positive result, as an exploratory measure we built a classifier that attempted to distinguish between metastatic tumors and non-metastatic lesions.
To consider the normative implications of the choice of IRHI measure, we build on the observation that different measures embody different IRHI equivalence criteria reflecting alternative vertical equity judgements. 2 An IRHI equivalence criterion specifies how, given the joint distribution of health and income, an additional amount of health should be distributed so as to leave IRHI unchanged.
For different types of seed measure groups, we build an addition model as shown in Eq. 2: (2) S i m (g 1, g 2 ) = ∑ H i ⋅ R a n k S i m (i ) + H α ⋅ m a x + H β ⋅ m i n + H γ ⋅ a v e if type = high; ∑ L i ⋅ R a n k S i m (i ) + L α ⋅ m a x + L β ⋅ m i n + L γ ⋅ a v e if type = low; ∑ M i ⋅ R a n k S i m (i ) + M α ⋅ m a x + M β ⋅ m i n + M γ ⋅ a v e if type = mix.
For definition of our quality measure for test cases, we built upon executable code coverage strategies.
We built repeated measures ANOVAs to test both between group (photoperiod treatment) and within individual effects.
None of the household factors we built to measure common eating habits or common exposures to cat or soil was able to explain that heterogeneity among households.
In our practice, we built a simulator to measure the performance of the proposed DAWP cache.
"Yes, we do want to take a game at least off this Australian side - but we also need to look at other measures to ensure we build on success, not undermine it".
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com