Consistency of support vector machines for forecasting the evolution of an unknown ergodic dynamical system from observations with unknown noise
未知噪声下遍历动力系统预测的支持向量机一致性
We consider the problem of forecasting the next (observable) state of an unknown ergodic dynamical system from a noisy observation of the present state. Our main result shows, for example, that support vector machines (SVMs) using Gaussian RBF kernels can learn the best forecaster from a sequence of noisy observations if (a) the unknown observational noise process is bounded and has a summable α-mixing rate and (b) the unknown ergodic dynamical system is defined by a…