This monograph reviews different methods to design or learn valid kernel functions for multiple outputs, paying particular attention to the connection between p

A unique introduction to reproducing kernel Hilbert spaces, covering the fundamental underlying theory as well as a range of applications.

The book covers theoretical questions including the latest extension of the formalism, and computational issues and focuses on some of the more fruitful and pro

Reproducing kernel Hilbert spaces have developed into an important tool in many areas, especially statistics and machine learning, and they play a valuable role