Summary Measuring the similarity of graphs is a fundamental step in the analysis of graph-structured data, which is omnipresent in computational biology. Graph kernels have been proposed as a powerful and efficient approach to this problem of graph comparison. Here we provide graphkernels, the first R and Python graph kernel libraries including baseline kernels such as label histogram based ker...

* Corresponding author – [email protected]. Graduate student at TUM, work carried out the GE-Global research working towards a master thesis at TUM. Abstract: Many high performance computing algorithms are bandwidth limited, hence the need for optimal data rearrangement kernels as well as their easy integration into the rest of the application. In this work, we have built a CUDA library of fas...

We introduce a new method of constructing kernels on sets whose elements are discrete structures like strings trees and graphs The method can be applied iteratively to build a kernel on a in nite set from kernels involving generators of the set The family of kernels generated generalizes the family of radial basis kernels It can also be used to de ne kernels in the form of joint Gibbs probabili...

The main purpose of this paper is providing a systematic study and classification of non-scalar kernels for Reproducing Kernel Hilbert Spaces (RKHS), to be used in the analysis of deformation in shape spaces endowed with metrics induced by the action of groups of diffeomorphisms. After providing an introduction to matrix-valued kernels and their relevant differential properties, we explore exte...

Selecting a particular summative (i.e., formally equivalent to a probability distribution) kernel when filtering a digital signal can be a difficult task. To circumvent this difficulty, one can work with maxitive (i.e., formally equivalent to a possibility distribution) kernels. These kernels allow to consider at once sets of summative kernels with upper bounded bandwith. They also allow to per...

As variations in kernel uniformity can affect rice milling performance, limited thickness-grading to remove thin kernels was evaluated for effects on milling yields. Along with unfractioned (UNF) rice of four long-grain cultivars, rough rice was mechanically sieved, resulting in two thickness fractions, Thick (>0.079 inch) and Thin (<0.079 inch). Milled rice yield (MRY) and head rice yield (HRY...

In this article, we propose a family of efficient kernels for large graphs with discrete node labels. Key to our method is a rapid feature extraction scheme based on the Weisfeiler-Lehman test of isomorphism on graphs. It maps the original graph to a sequence of graphs, whose node attributes capture topological and label information. A family of kernels can be defined based on this Weisfeiler-L...

Over the last few years, two different notions of positive definite (pd) kernels—universal and characteristic—have been developing in parallel in machine learning: universal kernels are proposed in the context of achieving the Bayes risk by kernel-based classification/regression algorithms while characteristic kernels are introduced in the context of distinguishing probability measures by embed...

Kernel Methods are known for their robustness in handling large feature space and are widely used as an alternative to external feature extraction based methods in tasks such as classification and regression. This work follows the approach of using different string kernels such as n-gram kernels and gappy-n-gram kernels on text classification. It studies how kernel concatenation and feature com...

Apical florets of maize (Zea mays L.) ears differentiate later than basal florets and form kernels which have lower dry matter accumulation rates. The purpose of this study was to determine whether increasing the temperature of apical kernels during the dry matter accumulation period would alter the difference in growth rate between apical and basal kernels. Apical regions of field-grown maize ...