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We propose a kernelized maximal-figure-of-merit (MFoM) learning approach to efficiently training a nonlinear model using subspace distance minimization. In particular, a fixed, small number of training samples are chosen in a way that the distance between function spaces constructed with a subset of training samples and with the entire training data set is minimized. This construction of the subset enables us to learn a nonlinear model efficiently while keeping the resulting model nearlydoi:10.1109/icassp.2011.5946732 dblp:conf/icassp/ByunL11 fatcat:4tt2iyud6fcyvkbwiwt37w33re