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.

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

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