| http://www.w3.org/ns/prov#value | - 1where ??i is a Lagrange multiplier, which can be thought of as a weight for example x, and ?? is a parameter that controls the trade-off to maximize the distance of the hyper-plane (or hyper-sphere) from the origin (background data) and also maximize the number of data points (target data) contained by the hyper-plane; l is the number of points in the training dataset, and K(xi,xj) is the kernel
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