Metric learning for kernel ridge regression: assessment of molecular similarity

Metric Learning for Kernel Regression

Kernel regression is a well-established method for nonlinear regression in which the target value for a test point is estimated using a weighted average of the surrounding training samples. The weights are typically obtained by applying a distance-based kernel function to each of the samples, which presumes the existence of a well-defined distance metric. In this paper, we construct a novel alg...

Similarity Metric Learning for aVariable - Kernel Classi

Nearest-neighbour interpolation algorithms have many useful properties for applications to learning, but they often exhibit poor generalization. In this paper, it is shown that much better generalization can be obtained by using a variable interpolation kernel in combination with conjugate gradient optimization of the similarity metric and kernel size. The resulting method is called variable-ke...

Kernel Regression with Sparse Metric Learning

Kernel regression is a popular non-parametric fitting technique. It aims at learning a function which estimates the targets for test inputs as precise as possible. Generally, the function value for a test input is estimated by a weighted average of the surrounding training examples. The weights are typically computed by a distancebased kernel function and they strongly depend on the distances b...

The Analysis of Kernel Ridge Regression Learning Algorithm

Similarity Metric Learning for a Variable-kernel Classiier

Nearest-neighbour interpolation algorithms have many useful properties for applications to learning, but they often exhibit poor generalization. In this paper, it is shown that much better generalization can be obtained by using a variable interpolation kernel in combination with conjugate gradient optimization of the similarity metric and kernel size. The resulting method is called variable-ke...