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The five factors were labelled Learning Environment (Factor 1), Reflective Practice (Factor 2), Feedback (Factor 3) and Patient Management (Factor 4) and Modelling (Factor 5).
The five factors identified in the present study were labelled: Learning Environment (Factor 1), Reflective Practice (Factor 2), Feedback (Factor 3) and Patient Management (Factor 4) and Modelling (Factor 5).
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Argyris (1976) labels learning as either "single -or "double-loop" learning.
To tackle this problem, we propose a Two Stage Architecture (TSA) for efficient multi-label learning.
This paper proposes a novel multi-label learning method inspired by the classifier chain approach.
Building models with this type of data is referred to as "multi-label learning" [10].
The experimental results are validated using several nonparametric statistical tests and confirm the effectiveness of the proposal for a better multi-label learning.
Our practice on game props recommendation reflects the enormous potential of the multi-instance multi-label learning framework in commercial applications.
The present study aimed to investigate the impact of a novel hybrid algorithm consisting of Gases Brownian Motion optimization (GBMO) algorithm and the gradient based fast converging parameter estimation method on multi-instance multi-label learning.
We deal with game prop recommendation under the multi-instance multi-label learning framework directly against the complicated dependencies and long-distance interventions, and minimize the ranking error to meet the requirements of the props priority-role dependencies.
Applying distinct multi-label learning methods allowed to extract crucial characteristics of the compounds that are selective inhibitors of any of the two targets and to build models with good predictivity.
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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