Reproducing kernel Hilbert spaces

A Reproducing Kernel Hilbert Space (RKHS) is a mathematical framework that allows functions to be analyzed and learned using kernel functions. Think of it as a space where each point's influence on functions is measured through a special function called a kernel, which captures the similarity between data points. This approach simplifies complex computations, enabling efficient learning in machine learning tasks like pattern recognition. Essentially, RKHS provides a structured way to work with functions based on pairwise similarities, making it a powerful tool for modeling and understanding data relationships in high-dimensional spaces.