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:<math>f(x)=\sum_{i=1}^N\sum_{m=1}^P\alpha_i^mK_m(x_i^m,x^m)+b</math>
The parameters <math>\alpha_i^m</math> and <math>b</math> are learned by gradient descent on a coordinate basis. In this way, each iteration of the descent algorithm identifies the best kernel column to choose at each particular iteration and adds that the the combined kernel. The model is then rerun to generate the optimal weights <math>\alpha_i</math> and <math>b</math>.
===Semisupervised learning===
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