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This study is one of the approaches to derive training algorithm of FRBF Network approach which has fuzzy input, fuzzy output and fuzzy weights, as an alternative to FR methods in the literature.
The authors could assert that the dataset is imbalanced in the number of classes, and it represents an obstacle to derive training and test datasets.
Specifically, the Avitabile map used the NBS field plots and the NBS LC map to estimate biomass density while the Baccini map used the NBS biomass map to derive training data.
To derive training labels (Additional File 1, Figure S2), we used data available from two BPDE studies in human cell lines [ 47, 48], combining the P values from the studies using Fisher's method.
The results of our feasibility study [ 80] allowed us to derive training group effect sizes; these ranged from 0.45-1.1 on the SocialVille measures, from 0.53-0.77 for the social cognitive measures and from 0.4-0.67 for the generalization and functional measures.
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The focus of this paper is therefore the development of algorithms to derive train location information from measured speed profile data and network topology.
The effectiveness of the derived training algorithm is demonstrated by computation of three numerical examples performed for proposed FRBF Network approach using the Backpropagation algorithm.
Second, to further improve estimates of the HMM parameters, we proceeded with validation of the automatically derived training set.
Deriving training content from a single set of cross-cutting competencies offers both theoretical and practical advantages over utilization of distinct competency sets for each type of healthcare worker being trained.
The mask method could automatically derive more training examples from the original training data, which significantly improves system performance.
Therefore pre-processing to derive a training set with balanced active/inactive data is not required.
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