Examples of using Hyperplane in English and their translations into Ukrainian
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A hyperplane in ℜn divides ℜn into two pieces;
Then f(x)=0 if, and only if, x is on the hyperplane.
Suppose the hyperplane passes through h and is parallel to S, and let s1.
Geometrically, f( x){\displaystyle f(x)} defines a hyperplane in Rn.
Some subsequence of these hyperplanes converges to the desired tangent hyperplane.
Moreover, a line segment from one side to the other passes through the hyperplane.
Any hyperplane can be written as the set of points x→{\displaystyle{\vec{x}}} satisfying.
A convex set inℜn has at least one tangent hyperplane at every boundary point.
The optimal separating hyperplane is the one that maximizes the distance between the two parallel hyperplanes.
A stellation diagram of an n-polytope exists in an(n-1)-dimensional hyperplane of a given facet.
That hyperplane is the boundary between the set of all points closer to a than to b, and the set of all points closer to b than to a.
A closed half-space is the union of an open half-space and the hyperplane that defines it.
The hyperplane H(x) is therefore called a supporting hyperplane with exterior(or outer) unit normal vector x.
Where w→{\displaystyle{\vec{w}}} is the(not necessarily normalized)normal vector to the hyperplane.
So we choose the hyperplane so that the distance from it to the nearest data point on each side is maximized.
This means that the residuals vector lies on an(N- 1)-dimensional hyperplane Sz that is perpendicular to z.
The further from the hyperplane our data points lie, the more confident we are that they have been correctly classified.
Now assume, for purpose of contradiction, that there is a point d in C on the hyperplane or on the same side as x.
Although the classifier is a hyperplane in the transformed feature space, it may be nonlinear in the original input space.
Given a nonempty convex set C in ℜn and a pointx in the exterior of C, there is a hyperplane separating C from x; i.
More formally, a support vector machine constructs a hyperplane or set of hyperplanes in a high or infinite dimensional space, which can be used for classification, regression or other tasks.
It is also one of the simplest examples of a hypersimplex,a polytope formed by certain intersections of a hypercube with a hyperplane.
As a very simple example, for a classification task with just two features(such as the image above),you can imagine a hyperplane for a line that linearly separates and classifies a set of information.
However, it may not always be easy to graphically depict solutions sets- for example, the solution set to an equation in the form ax+ by+ cz+ dw= k(with a, b, c, d, and k real-valued constants)is a hyperplane.
An important consequence of this geometricdescription is that the max-margin hyperplane is completely determined by those x→ i{\displaystyle{\vec{x}}_{i}} that lie nearest to it.
The desired partial correlation is then the cosine of the angle φ between the projections rX and rY of x and y,respectively, onto the hyperplane perpendicular to z.[2]: ch.
Note the fact that the set of points x{\displaystyle x}mapped into any hyperplane can be quite convoluted as a result, allowing much more complex discrimination between sets that are not convex at all in the original space.
The set of the points at infinity is called, depending on the dimension of the space, the line at infinity,the plane at infinity or the hyperplane at infinity, in all cases a projective space of one less dimension.
In mathematics, the vertex enumeration problem for a polytope,a polyhedral cell complex, a hyperplane arrangement, or some other object of discrete geometry, is the problem of determination of the object's vertices given some formal representation of the object.
