Chunking for Massive Nonlinear Kernel Classification
Abstract
A chunking procedure [2] utilized in [18] 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 [15] is developed here as a completely
unconstrained minimization problem [17]. 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 [12] are used.
Because a 1-norm support vector machine underlies the proposed method, the approach together with
a reduced support vector machine formulation [13] minimizes the number of kernel functions utilized
to generate a simplified nonlinear classifier.
Subject
dual penalty
linear programming
massive datasets
nonlinear kernel
classification
Permanent Link
http://digital.library.wisc.edu/1793/64342Citation
06-07