Design and comparative study of the RKHS model reduction techniques

The paper proposes the design and comparative study of two reduction methods of these models. The first, titled support vector regression (SVR) and the second is the projection method. Both methods use the Statistical Learning Theory (SLT) which operates on Reproducing Kernel Hilbert Space (RKHS) space. The performances of both methods are evaluated on the Tennessee Eastman process.