Regularised Least-Squares Regression with Infinite-Dimensional Output Space

10/21/2020
by   Junhyunng Park, et al.
0

We present some learning theory results on reproducing kernel Hilbert space (RKHS) regression, where the output space is an infinite-dimensional Hilbert space.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/15/2022

A short note on compact embeddings of reproducing kernel Hilbert spaces in L^2 for infinite-variate function approximation

This note consists of two largely independent parts. In the first part w...
research
11/08/2011

The theory and application of penalized methods or Reproducing Kernel Hilbert Spaces made easy

The popular cubic smoothing spline estimate of a regression function ari...
research
05/26/2023

Bilipschitz group invariants

Consider the quotient of a real Hilbert space by a subgroup of its ortho...
research
05/28/2018

Autoencoding any Data through Kernel Autoencoders

This paper investigates a novel algorithmic approach to data representat...
research
09/28/2022

Minimax Optimal Kernel Operator Learning via Multilevel Training

Learning mappings between infinite-dimensional function spaces has achie...
research
05/31/2021

Control Occupation Kernel Regression for Nonlinear Control-Affine Systems

This manuscript presents an algorithm for obtaining an approximation of ...
research
09/11/2020

A kernel function for Signal Temporal Logic formulae

We discuss how to define a kernel for Signal Temporal Logic (STL) formul...

Please sign up or login with your details

Forgot password? Click here to reset