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 Equation 4 ∞, = sgnI EoliYi (potato? x) +6 4.1.2 non-linear classification of linear classifier is only suitable in the case of linearly separable, the problem is often encountered in reality is linearly inseparable, at this time linear classification method is not applicable to the nonlinear case in support vector machine classification method similar to the actual and the linear case, and still is to find the margin the largest hyperplane, but this was the original sample space is mapped to a high-dimensional space, and then in this higher dimensional space to find the optimal classification surface strike and description of non-linear decision boundaries are very difficult, and difficult to be parameterized, so a reasonable solution is that we can whether two types of 42 Zhejiang University. Whether there is a line to see a small sign of Pt, such as Pt900 and Pt950 the inner ring of the mens cufflinks.
A master's degree thesis in Chapter 4 the classification of surface color recognition between the samples based on SVM classifiers to describe the nonlinear case with a linear decision boundaries in the original feature space (D dimensional) decision boundaries between the two types of samples is a nonlinear problem, apparently by a linear decision boundary can not be directly used to describe non-linear boundary between the two types of samples, but by some non-linear characteristics of nonlinear problems in the original feature space transform, nonlinear problems in the original feature space to transform the linear problem in a high-dimensional space, thus in this high-dimensional space constructed generalized optimal hyperplane obtained in the transformed linear solution in the high dimensional space. Necklace clasps or the back of the mens cufflinks can be found on the back. A variety of promotional tools of the cufflinks
If this hyperplane is not only able to correctly separate two types of data two types of samples from the distance between the hyperplane recent data is the largest of all hyperplane,