In numerous domains it is useful to represent a single example by the set of the local
features or parts that comprise it. However, this representation poses a challenge to many …

Discriminative learning is challenging when examples are sets of features, and the sets vary
in cardinality and lack any sort of meaningful ordering. Kernel-based classification methods …

Pyramid intersection is an efficient method for computing an approximate partial matching
between two sets of feature vectors. We introduce a novel pyramid embedding based on a …

The kernel function plays a central role in kernel methods. Existing methods typically fix the
functional form of the kernel in advance and then only adapt the associated kernel …

Similarity measure is fundamental to many machine learning and data mining algorithms.
Predefined similarity metrics are often data-dependent and sensitive to noise. Recently, data …

Kernel functions have become an extremely popular tool in machine learning, with an
attractive theory as well. This theory views a kernel as implicitly mapping data points into a …

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 …

Learning a measure of similarity between pairs of objects is a fundamental problem in
machine learning. It stands in the core of classification methods like kernel machines, and is …

Kernel functions are typically viewed as providing an implicit mapping of points into a high-
dimensional space, with the ability to gain much of the power of that space without incurring …

Kernel approximation using randomized feature maps has recently gained a lot of interest. In
this work, we identify that previous approaches for polynomial kernel approximation create …