Frédéric Protin

A Framework for Supervised and Unsupervised Learning via Reproducing Kernel Hilbert Spaces

We introduce a unified framework for supervised and unsupervised learning through density estimation using reproducing kernel Hilbert spaces (RKHSs). To this end, we introduce the notion of a nested RKHS, that is, an increasing sequence of RKHSs whose union is dense in an ambient $L^2$-space. This construction allows u...