Bounds on the Rate of Uniform Convergence of Learning Process on Possibility Spaces
Peng Wang · Journal of Hebei University · 2004
Statistical learning theory on probability spaces is an important part of Machine Learning. In the statistical learning theory on probability spaces, the bounds on the rate of uniform convergence have significant meanings. They determine generalization abilities of the learning machines utilizing the empirical risk minimization induction principle. In this paper, we discuss the bounds on the risk for indicator loss function on possibility space, and then estimate the rate of uniform convergence and finally point out the relation between the rate of convergence and the capacity of a set of function.