Sequential Minimal Optimization
where K is a kernel function that measures the similarity or distance between the input vector and the stored training vector . Examples of K include Gaussians, polynomials, and neural network non-linearities [4]. If K is linear, then the equation for the linear SVM is recovered. ................
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