We consider an ensemble of N discrete nonintersecting paths starting from equidistant points and ending at consecutive integers. Our first result is an explicit formula for the correlation kernel that allows us to analyze the process as N → ∞. In that limit we obtain a new general class of kernels describing the local correlations close to the equidistant starting points. As the distance betwee...

Subspace-based learning problems involve data whose elements are linear subspaces of a vector space. To handle such data structures, Grassmann kernels have been proposed and used previously. In this paper, we analyze the relationship between Grassmann kernels and probabilistic similarity measures. Firstly, we show that the KL distance in the limit yields the Projection kernel on the Grassmann m...

In most kernel based machine learning algorithms and non-parametric statistics the key computational task is to compute a linear combination of local kernel functions centered on the training data, i.e., f(x) = ∑N i=1 qik(x, xi), which is the discrete Gauss transform for the Gaussian kernel. f is the regression/classification function in case of regularized least squares, Gaussian process regre...

OBJECTIVE To describe and quantify growth patterns of cholesteatomas within the middle ear using a scaled rating system that characterizes patterns of ossicular erosion. STUDY DESIGN Retrospective case review. SETTING Tertiary referral center. PATIENTS Patients undergoing first-time surgery for primary and secondary acquired cholesteatomas. INTERVENTIONS Intraoperative assessments of os...

This paper derives and analyzes continuous time random walk (CTRW) models in radial flow geometries for the quantification of non-local solute transport induced by heterogeneous flow distributions and by mobile-immobile mass transfer processes. To this end we derive a general CTRW framework in radial coordinates starting from the random walk equations for radial particle positions and times. Th...

This work explores the application of kernel based local meshless method for solving multi-dimensional wave equations in irregular domain. The method is tested for various types of boundary conditions in irregular shaped domain. The method is capable of solving multi-dimension large scaled problems in complex shaped domain.

We describe the system submitted to IBEREVAL-2017 for stance and gender detection in tweets on Catalan Independence [1]. We developed a supervised system using Support Vector Machines with radial basis function kernel to identify the stance and gender of the tweeter using various character level and word level features. Our system achieves a macro-average of F-score(FAVOR) and F-score(AGAINST) ...

The prediction of lithology is necessary in all areas of petroleum engineering. This means that to design a project in any branch of petroleum engineering, the lithology must be well known. Support vector machines (SVM’s) use an analytical approach to classification based on statistical learning theory, the principles of structural risk minimization, and empirical risk minimization. In this res...

Automatic classification of fruits via computer vision is still a complicated task due to the various properties of numerous types of fruits. We propose a novel classification method based on a multi-class kernel support vector machine (kSVM) with the desirable goal of accurate and fast classification of fruits. First, fruit images were acquired by a digital camera, and then the background of e...

moment descriptors are commonly used in applications such as image classification, pattern recognition and identification. A radial-polar representation of image coordinate space is particularly useful in the above applications, since it facilitates the derivation of rotation invariants of any arbitrary order. Zernike moments and radial-Tchebichef moments fall into the category of moments that ...