‎In this article‎, ‎we develop two nonparametric smoothing estimators for parameter of a time-variant parametric model‎. ‎This parameter can be from any parametric family or from any parametric or semi-parametric regression model‎. ‎Estimation is based on a two-step procedure‎, ‎in which we first get the raw estimate of the parameter at a set of disjoint time...

Face recognition is always a hot topic in research. In this paper, we represent a robust method of face recognition using gabor feature extraction, kernel PCA and K-NN classifier. Gabor features are calculated for each face images then it’s polynomial kernel function is calculated, it is directly applied to the K-NN classifier. The effectiveness of the proposed method is demonstrated by the exp...

We prove that a two-sided sub-Gaussian estimate of the heat kernel on an infinite weighted graph takes place if and only if the volume growth of the graph is uniformly polynomial and the Green kernel admits a uniform polynomial decay.

<span id="docs-internal-guid-10508d4e-7fff-5011-7a0e-441840e858c8"><span>This paper compares the fuzzy kernel k-medoids using radial basis function (RBF) and polynomial in hepatitis classification. These two functions were chosen due to their popularity any kernel-based machine learning method for solving classification task. The dataset then used evaluate performance of both method...

Masry (1996b) provides estimation bias and variance expression for a general local polynomial kernel estimator in a general multivariate regression framework. Under smoother conditions on the unknown regression and by including more refined approximation terms than that in Masry (1996b), we extend the result of Masry (1996b) to obtain explicit leading bias terms for the whole vector of the loca...

In this paper, a new kernel function is introduced that improves the classification accuracy of support vector machines (SVMs) for both linear and non-linear data sets. The proposed kernel function, called Gauss radial basis polynomial function (RBPF) combine both Gauss radial basis function (RBF) and polynomial (POLY) kernels. It is shown that the proposed kernel converges faster than the RBF ...

Decomposition of the polynomial kernel of arbitrary higher spin Dirac operators D. Eelbode, a) T. Raeymaekers, b) and J. Van der Jeugt c) Department of Mathematics and Computer Science, University of Antwerp, Campus Middelheim, G-Building, Middelheimlaan 1, 2020 Antwerpen, Belgium Clifford Research Group, Department of Mathematical Analysis, Ghent University, Galglaan 2, 9000 Ghent, Belgium Dep...

SUMMARY Classification tasks in computer vision and brain-computer interface research have presented several applications such as biometrics and cognitive training. However, like in any other discipline, determining suitable representation of data has been challenging, and recent approaches have deviated from the familiar form of one vector for each data sample. This paper considers a kernel be...

A graph is d-degenerate if its every subgraph contains a vertex of degree at most d. For instance, planar graphs are 5-degenerate. Inspired by recent work by Philip, Raman and Sikdar, who have shown the existence of a polynomial kernel for DOMINATING SET in d-degenerate graphs, we investigate kernelization hardness of problems that include connectivity requirement in this class of graphs. Our m...

In a parameterized problem, every instance I comes with a positive integer k. The problem is said to admit a polynomial kernel if, in polynomial time, one can reduce the size of the instance I to a polynomial in k, while preserving the answer. In this work we give two meta-theorems on kernelzation. The first theorem says that all problems expressible in Counting Monadic Second Order Logic and s...