The sheer size of commodity operating system kernels makes them a prime target for local attackers aiming to escalate privileges. At the same time, as much as 90% of kernel functions are not required for processing system calls originating from a typical network daemon. This results in an unnecessarily high exposure. In this paper, we introduce kRazor, an approach to reduce the kernel’s attack ...

It is well known that each kernel function defines an interior-point algorithm. In this paper we propose new classes of kernel functions whose form is different from known kernel functions and define interior-point methods (IPMs) based on these functions whose barrier term is exponential power of exponential functions for P∗(κ )-horizontal linear complementarity problems (HLCPs). New search dir...

Squared-loss mutual information (SMI) is a robust measure of the statistical dependence between random variables. The sample-based SMI approximator called least-squares mutual information (LSMI) was demonstrated to be useful in performing various machine learning tasks such as dimension reduction, clustering, and causal inference. The original LSMI approximates the pointwise mutual information ...

The kernel technique is a powerful tool for constructing new pattern analysis methods. Kernel engineering provides a general approach to incorporating domain knowledge and dealing with discrete data structures. Kernel methods, especially the support vector machine (SVM), have been extensively applied in the bioinformatics field, achieving great successes. Meanwhile, the development of kernel me...

Recently, so-called self-regular barrier functions for primal-dual interior-point methods (IPMs) for linear optimization were introduced. Each such barrier function is determined by its (univariate) self-regular kernel function. We introduce a new class of kernel functions. The class is defined by some simple conditions on the kernel function and its derivatives. These properties enable us to d...

We introduce a new family of positive-definite kernel functions that mimic the computation in large, multilayer neural nets. These kernel functions can be used in shallow architectures, such as support vector machines (SVMs), or in deep kernel-based architectures that we call multilayer kernel machines (MKMs). We evaluate SVMs and MKMs with these kernel functions on problems designed to illustr...

By re-defining the inner product of a reproducing kernel space, the reproducing kernel functions of that space can be represented by form of polynomials without changing any other conditions, and the higher order of the derivatives, the simpler of the reproducing kernel function expressions. Such expressions of reproducing kernel functions are the simplest from the computational point of view, ...

of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy NONLINEAR SIGNAL PROCESSING BASED ON REPRODUCING KERNEL HILBERT SPACE By Jianwu Xu December 2007 Chair: Jose C. Principe Major: Electrical and Computer Engineering My research aimed at analyzing the recently proposed correntropy function...

Kernel methods use kernel functions to provide nonlinear versions of different methods in machine learning and data mining, such as Principal Component Analysis and Support Vector Machines. These kernel functions require the calculation of some or all of the entries of a matrix of the form XX . The formation of this type of matrix is known to result in potential numerical instability in the cas...

For analytic functions the remainder term of Gaussian quadrature rules can be expressed as a contour integral with kernel Kn. In this paper the kernel is studied on elliptic contours for a great variety of symmetric weight functions including especially Gegenbauer weight functions. First a new series representation of the kernel is developed and analyzed. Then the location of the maximum modulu...