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Abstract:
A new decomposition algorithm for training regression Support
Vector Machines (SVM) is presented. The algorithm builds on the
basic principles of decomposition proposed by Osuna et. al., and
addresses the issue of optimal working set selection. The new
criteria for testing optimality of a working set are derived. Based
on these criteria, the principle of "maximal inconsistency" is
proposed to form (approximately) optimal working sets. Experimental
results show superior performance of the new algorithm in
comparison with traditional training of regression SVM without
decomposition. Similar results have been previously reported on
decomposition algorithms for pattern recognition SVM. The new
algorithm is also applicable to advanced SVM formulations based on
regression, such as density estimation and integral equation
SVM.
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