Fully adaptive kernel-based methods

Adaptive Kernel Based Machine Learning Methods

During the support period July 1, 2011 June 30, 2012, seven research papers were published. They consist of three types: • Research that directly addresses the kernel selection problem in machine learning [1, 2]. • Research that closely relates to the fundamental issues of the proposed research of this grant [3, 4, 5, 6]. • Research that is in the general context of computational mathematics [7...

Adaptive ADER Methods Using Kernel-Based Polyharmonic Spline WENO Reconstruction

An adaptive ADER finite volume method on unstructured meshes is proposed. The method combines high order polyharmonic spline WENO reconstruction with high order flux evaluation. Polyharmonic splines are utilised in the recovery step of the finite volume method yielding a WENO reconstruction that is stable, flexible and optimal in the associated Sobolev (BeppoLevi) space. The flux evaluation is ...

Kernel Based Meshless Methods

Kernel–Based Meshless Methods

2 Kernels 1 2.1 Basics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2.2 Positive Definiteness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.3 General Rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.4 Inner Product . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.5 Dualit...

Adaptive Kernel Methods Using the Balancing Principle

The regularization parameter choice is a fundamental problem in Learning Theory since the performance of most supervised algorithms crucially depends on the choice of one or more of such parameters. In particular a main theoretical issue regards the amount of prior knowledge needed to choose the regularization parameter in order to obtain good learning rates. In this paper we present a paramete...