We review machine learning methods employing positive definite kernels. These methods
formulate learning and estimation problems in a reproducing kernel Hilbert space (RKHS) of …

We propose a framework for analyzing and comparing distributions, which we use to
construct statistical tests to determine if two samples are drawn from different distributions …

Pattern Analysis is the process of finding general relations in a set of data, and forms the
core of many disciplines, from neural networks, to so-called syntactical pattern recognition …

We propose two statistical tests to determine if two samples are from different distributions.
Our test statistic is in both cases the distance between the means of the two samples …

We describe a technique for comparing distributions without the need for density estimation
as an intermediate step. Our approach relies on mapping the distributions into a reproducing …

This paper introduces a new framework for image classification using local visual
descriptors. The pipeline first performs a nonlinear feature transformation on descriptors …

A detailed overview of current research in kernel methods and their application to
computational biology. Modern machine learning techniques are proving to be extremely …

We extend the herding algorithm to continuous spaces by using the kernel trick. The
resulting" kernel herding" algorithm is an infinite memory deterministic process that learns to …

Many real world graphs, such as the graphs of molecules, exhibit structure at multiple
different scales, but most existing kernels between graphs are either purely local or purely …

The advantages of discriminative learning algorithms and kernel machines are combined
with generative modeling using a novel kernel between distributions. In the probability …