Hybrid SVM-GPs learning for modeling of mitogen-activated protein kinases systems with noise

Abstract

In this paper, the hybrid support vector machines (SVM) and Gaussian process (GPs) are proposed to modeling of mitogen-activated protein kinases systems with noise. In the proposed approach, there are two-stage strategies. In stage 1, the support vector machine regression (SVMR) approach is used to filter out the some larger data set in the mitogen-activated protein kinases systems data set with noise. Because of the larger noise data in the training data set are almost removed, the large noise data's effects are reduce, so the concepts of robust statistic theory are not used to reduce the large noise data's effects. The rest of the training data set after stage 1 is directly used to training the Gaussian process for regression (GPR) in stage 2. According to the simulation results, the performance of the proposed approach is superior to the least squares support vector machines for regression, and GPR when the noise is existed in the mitogen-activated protein kinases systems.