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The most effective classification was provided by the combination of CDK FP with SMO or Random Forest algorithms.
However, while SVM can be considered the most effective classification model available in the ML area, several powerful options exist to tackle regression problems; in this regard, the works by Redi et al. [16], Le Callet et al. [8], and Staelens et al. [12] represent interesting examples.
We recommend that the most effective classification method is chosen on a study-to-study basis, according to the characteristics of the data.
Careful analysis of untilted images exploiting the most effective classification approaches should make it possible to recover a sufficient range of projections from untilted images for 3D analysis.
As clinical heterogeneity further obscures genotype phenotype correlations, a molecular genetic analysis of the gene set involved in FADS/LMPS is probably the most effective classification method for this disease spectrum.
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One of the most effective algorithm is Classification and Regression Trees [ 35].
The latter one turned out to be the most effective one for sample classification.
Before having the experiment, we applied each model to the validation dataset and examined which model is the most effective for the most precise classification.
It is found that features related to the variations of accelerations and decelerations (e.g., the proportions of accelerations and decelerations larger than 1 meter per square second, and the standard deviations of accelerations and decelerations) are the most effective in terms of vehicle classification using GPS data.
We found that classification was most effective with 1,000 bins, and that 10 kb windows yielded a good balance between resolution and accuracy (supplementary table S2, Supplementary Material online).
Recently, Deep Convolutional Neural Network (DCNN) has been recognized as the most effective model for pattern recognition and classification tasks.
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