In this paper we show how the kernel matrix can be learned from data via semidefinite programming (SDP) techniques.

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points.

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points.

The re- sulting kernel matrix can then be used in combination with a number of existing learning algorithms that use kernels, for example support vector ...

In this paper we show how the kernel matrix can be learned from data via semidefinite programming (SDP) techniques.

Abstract: Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data ...

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points.

This paper shows how the kernel matrix can be learned from data via semidefinite programming (SDP) techniques and leads directly to a convex method for ...

Sep 18, 2017 · Kernel-based learning algorithms are widely used in machine learning for problems that make use of the similarity between object pairs.

Dec 20, 2023 · “Learning the Kernel Matrix with Semidefinite Programming.” In: The Journal of Machine Learning Research, 5. Subject Headings: Kernel Matrix ...