Learning with Additional Distributions

摘要

This paper studies the problem of learning with distributions. In this work, we do not focus on the distribution that represents each data point. Instead, we consider the distribution that is an additional information around each data point. The proposed method yields a new kernel that is similar to an existing one. The main difference is that our kernel requires an integration in the kernel space. Theoretically, the proposed method yields a better generalization compared to normal SVM.