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The margin helps to define where a line of text begins and ends. When a page is justified the text is spread out to be flush with the left and right margins. When two pages of content are combined next to each other (known as a two-page spread ), the space between the two pages is known as the gutter . [ 2 ] (
Vector-based devices, such as the vector CRT and the pen plotter, directly control a drawing mechanism to produce geometric shapes. Since vector display devices can define a line by dealing with just two points (that is, the coordinates of each end of the line), the device can reduce the total amount of data it must deal with by organizing the ...
The soft-margin support vector machine described above is an example of an empirical risk minimization (ERM) algorithm for the hinge loss. Seen this way, support vector machines belong to a natural class of algorithms for statistical inference, and many of its unique features are due to the behavior of the hinge loss.
This process occurred rapidly: by the mid-1990s, virtually all commercial type design had transitioned to digital vector drawing programs. Each glyph design can be drawn or traced by a stylus on a digitizing board, or modified from a scanned drawing, or composed entirely within the program itself. Each glyph is then in a digital form, either in ...
Sequential minimal optimization (SMO) is an algorithm for solving the quadratic programming (QP) problem that arises during the training of support-vector machines (SVM). It was invented by John Platt in 1998 at Microsoft Research. [1] SMO is widely used for training support vector machines and is implemented by the popular LIBSVM tool.
The margin for an iterative boosting algorithm given a dataset with two classes can be defined as follows: the classifier is given a sample pair (,), where is a domain space and = {, +} is the sample's label.
The plot shows that the Hinge loss penalizes predictions y < 1, corresponding to the notion of a margin in a support vector machine. In machine learning, the hinge loss is a loss function used for training classifiers. The hinge loss is used for "maximum-margin" classification, most notably for support vector machines (SVMs). [1]
In particular, support vector machines find a hyperplane that separates the feature space into two classes with the maximum margin. If the problem is not originally linearly separable, the kernel trick can be used to turn it into a linearly separable one, by increasing the number of dimensions. Thus a general hypersurface in a small dimension ...