Clinical datasets typically contain continuous, ordinal, categorical and binary variables. To model this type of datasets, linear kernel methods are generally used. However, the linear kernel has some disadvantages, which were tackled by the introduction of a clinical one. This work shows that the use of a clinical kernel can improve the performance of support vector machine survival models. In...

In a directed graph, a kernel is a subset of the vertices that is both independent and absorbing. Not all directed graphs have a kernel, and finding classes of graphs having always a kernel or for which deciding the existence of a kernel is polynomial has been the topic of many works in graph theory. We formalize some techniques to build a kernel in a graph with a clique-cutset, knowing kernels...

In this paper, a new collocation method, which is based on Boubaker polynomials, is introduced for the approximate solutions of a class of two-dimensional linear Fredholm integral equationsof the second kind. The properties of two-dimensional Boubaker functions are presented. The fundamental matrices of integration with the collocation points are utilized to reduce the solution of the integral ...

Two years ago, Ryan Weber developed a simple support vector machine for protein secondary structure prediction to investigate the complexity of kernel and algorithm necessary to perform adequate classification [1]. For my project, I have resurrected Ryan‘s project, tested his conclusions, and extended the support vector machine to use the spectrum string kernel designed by Leslie, Eskin, and No...

Abstract We investigate the anomalous behavior of localization length a non-interacting one-dimensional Anderson model at zero temperature. report numerical calculations Thouless expression length, based on Kernel polynomial method (KPM), which has an ...