Before revision: Computing the expectation of some kernel function is ubiquitous in machine learning, from the classical theory of support vector machines , to exploiting kernel embeddings of distributions in applications ranging from probabilistic modeling, statistical inference, casual discovery, and deep learning.
After revision: Computing the expectation of some kernel function is ubiquitous in machine learning, from the classical theory of support vector machines to exploiting kernel embeddings of distributions in applications ranging from probabilistic modeling, statistical inference, casual discovery, and deep learning.