SVM is a classification method that uses a maximum margin hyperplane from a set of hyperplanes in a high-dimensional space to separate two or more classes by distancing the plane from the closest data point of all classes.
The SVM finds a separating hyperplane in the feature space between two classes in such a way that the distance between the hyperplane and the closest data points of each class is maximized.
For a closer data analysis, the out-of-plane cuts were fitted according to the unified fit model [ 31– 31].
This optimal separating hyperplane (OSH) w: wx + b = 0 maximizes the margin of the closest data points.
Y 980115 is very close to the data of 400 °C heating, and B-7904 is between the data of 500 and 600 °C.
The lattice parameter determined from the positions of (003) and (004) reflections is a = 0.39069(18) nm, which is close to the data of Ref. [25] (a = 0.39050 nm).
The bureau also has proposed terminating a series of industrial reports, closing a data center, and closing six of its 12 regional offices.
In addition, OC estimates were closest to the sample data of permeability with the minimum variance.
Close inspection of biological data of the tested compounds led to several observations on their structure activity relationships.
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