These instances, whenmapped to an N-dimensional space, represent a core set that can be used to construct an approximation to theminimumenclosing ball. Solving the SVMlearning problem on these core sets can produce a good approximation solution in very fast speed. For example, the core-vector machine [81] thus produced can learn an SVM for millions of data in seconds.

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