Data Reduction in Support Vector Machines by a Kernelized Ionic Interaction Model
Hyunsoo Kim, Haesun Park · 2004
A major drawback of support vector machines is their high computational complexity. In this paper, we introduce a novel kernelized ionic interaction (IoI) model for data reduction in support vector machines. We also present a data reduction method based on the kernelized instance based (KIB2) algorithm. We show that the computation time can be significantly reduced without any significant decrease in the prediction accuracy.