Chunking for Massive Nonlinear Kernel Classification
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A chunking procedure  utilized in  for linear classifiers is proposed here for nonlinear kernel classification of massive datasets. A highly accurate algorithm based on nonlinear support vector machines that utilizes a linear programming formulation  is developed here as a completely unconstrained minimization problem . This approach together with chunking leads to a simple and accurate method for generating nonlinear classifiers for a 250000-point dataset that typically exceeds machine capacity when standard linear programming methods such as CPLEX  are used. Because a 1-norm support vector machine underlies the proposed method, the approach together with a reduced support vector machine formulation  minimizes the number of kernel functions utilized to generate a simplified nonlinear classifier.