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So the semi-equivalency is measured by grouping Sex, and Edu attributes, while Age attribute is anonymized as per interval.
In case of the "age" attribute it is practically impossible to perform a similar procedure, as a human cannot estimate the age value from a face image.
The basic idea of cumulative features is as follows: using support vector regression (SVR) [29]; using the idea of cumulative features [4] as the source data for the SVR; using a two-stage scheme of determining the "age" attribute [8].
Based on an analysis of various approaches to age classification by face images using regression analysis, the following action sequence that combines the best of currently available ideas is proposed: using support vector regression (SVR) [29]; using the idea of cumulative features [4] as the source data for the SVR; using a two-stage scheme of determining the "age" attribute [8].
For the second and third column (α j =0.5 and α j =1) age attribute favors n = 1 class (marked by blue curve), while RBC attribute favors (b / RBC) n = 3 class (marked by green curve).
The data is processed for every k th blood and age attribute separately, so every set X k, n represents the n th fibrosis class of k th attribute.
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