Given a set of high-resolution image examples y i,i=1…N, we generate the corresponding low-resolution images x i,i=1…N (in fact, we upscale them to the original size by bicubic interpolation).
For example, Y c i will be positive when the MDI24 score of a child is higher than predicted given the characteristics listed above.
For example, if y i, our outcome variable, is affected by a single variable, x, and we have two groups, urban and rural, then HAZ for the rural, and urban children are given by Eqs.
For example, (5) y i j = x i j ∑ j x i j 2 1 / 2 Treating each row in Y as a point, the points are then clustered into k subsets using the K-means clustering algorithm.
Figure 4 The reconstruction examples of y i.
As an example, here we describe how such search can be performed for the model y i = Λy i + e i.
The goal of training an SVM is learning to label a dataset just like the training examples (x (i ), y(i )), i=1,…, N. Here, x are the examples (sequences in our case), and y the labels.
For example, Y f - = def Y i / 2 i - 2, Y f + = def Y i / 2 i.
Let L be a set of labels, and D be a multi-label evaluation data set consisting of m multi-label examples (x i, Y i ), where i∈{1,…, m} and Y i ⊆ L. Let Z i be the set of predicted labels for x i.
