Sentence examples for transformation relevance from inspiring English sources

Table 1 Summary of the proposed approaches   Algorithm Transformation Relevance measure Method 1 Algorithm 1 1D–PCA ρ lc Method 2 Algorithm 1 1D–PCA ρ su Method 3 Algorithm 1 1D–PLS ρ lc Method 4 Algorithm 1 1D–PLS ρ su Method 5 Algorithm 2 2D–PCA ρ lc Method 6 Algorithm 2 2D–PCA ρ su Method 7 Algorithm 2 2D–PLS ρ lc Method 8 Algorithm 2 2D–PLS ρ su.

Many of the model complexes discussed in this review carry out oxidative transformations of relevance to the enzymatic reactions; however, the participation of a high-valent iron-oxo species (FeIVO or FeVO) can only be inferred.

Table 1 summarizes the eight different combinations accomplished for the proposed approaches, including the algorithm, the transformation and the relevance measure used in each case.

The multi-label multi-class Naïve Bayes model (MMM), with |Y| ≥ 2 and |L| > 2, was implemented based on Wei et al. [52], where a binary relevance transformation [39] was utilised.

Using Eqs. 2, 3, 4 and a binary relevance transformation, |L| "pseudo single-label" binary classifiers, H l  : χ → {l, ¬ l}, were constructed – one for each unique label l in the set L. (The term "pseudo single-label" is as defined and described in the Background section and in the paragraph below).

The performance of our approaches is compared with the performance of two algorithm adaptation methods (Multi-Label k-NN and Multi-Label C4.5) and five problem transformation methods (Binary Relevance, Classifier Chain, Calibrated Label Ranking with majority voting, the Quick Weighted method for pair-wise multi-label learning and the Label Powerset method).

Over-expression of upstream ERK1/2 modulators (such as MEK1) in normal mammary epithelial cells induces neoplastic transformation, highlighting the relevance of this signalling network to breast cancer [ 25].

Then, improved predictability is gained by using either classifiers chain or binary relevance transformations.

In this work, they focused on ranked keyword search, term frequencies, and a novel relevance score transformation function.

Here, the function in novel relevance score transformation hides the term-specific distribution of relevance score values, and it makes the scores of different terms indistinguishable.

Many processes do not even enter maps in the first place, due to distant spatial or temporal drivers, societal meaning and relevance, and transformation over time (although this is hidden in a map; Wood 2010).