# Understanding Kernel Mean Embeddings

I was learning about kernel mean embeddings. I was under the impression that the mean embedding was just the mean of a function with respect to a probability distribution. Then I saw somewhere that if you have a "one-hot" representation of a distribution the kernel embedding becomes a vector. Is this true? How can you use these mean embeddings like in SVMs or Ridge Regressions if they are vectors.