Kernel-based approaches for sequence classification have been successfully applied to a variety of domains, including the text categorization, image classification, speech analysis, biological sequence analysis, time series and music classification, where they show some of the most accurate results. Typical kernel functions for sequences in these domains (e.g., bag-of-words, mismatch, or subseq...

The kernel functions (kernels) can be used in many types of nonparametric methods estimation of the density function of a random variable, estimation of the hazard function or the regression function. These methods belong to the most efficient non-parametric methods. Another nonparametric method uses so-called frames overcomplet systems of functions of some type. This paper compares the kernel ...

We investigate the space of functions in which the BFKL kernel acts. For the amplitudes which describe the scattering of colorless projectiles it is convenient to define, in transverse coordinates, the Möbius space in which the solutions to the BFKL equation vanish as the coordinates of the two reggeized gluons coincide. However, in order to fulfill the bootstrap relation for the BFKL kernel it...

In this paper, we present an implementation of CLNP ground-to-ground packet processing for ATN in Linux kernel version 2.6. We present the big picture of CLNP packet processing, the details of input, routing, and output processing functions, and the implementation of each function based on ISO 84731. The functions implemented in this work are PDU header decomposition, header format analysis, he...

We study a 3–parametric family of stochastic point processes on the one–dimensional lattice originated from a remarkable family of representations of the infinite symmetric group. We prove that the correlation functions of the processes are given by determinantal formulas with a certain kernel. The kernel can be expressed through the Gauss hypergeometric function; we call it the hypergeometric ...

Kernel-based approaches for sequence classification have been successfully applied to a variety of domains, including the text categorization, image classification, speech analysis, biological sequence analysis, time series and music classification, where they show some of the most accurate results. Typical kernel functions for sequences in these domains (e.g., bag-of-words, mismatch, or subseq...

This Research presents the effects of interaction between various Kernel functions and different Feature Selection Techniques for improving the learning capability of Support Vector Machine (SVM) in detecting email spams. The interaction of four Kernel functions of SVM i. e. "Normalised Polynomial Kernel (NP)", "Polynomial Kernel (PK)", "Radial Basis Function Kernel (RB...

We study a positive reproducing kernel for holomorphic functions on complex domains. This kernel, which induces what has now come to be known as the Berezin transform, is manufactured from the Bergman kernel using an idea of L. K. Hua. The kernel has important analytic and geometric properties which we develop in some detail.

The kernel-based classifiers use one of the classical kernels, but the real-world applications have emphasized the need to consider a new kernel function in order to boost the classification accuracy by a better adaptation of the kernel function to the characteristics of the data. Our purpose is to automatically design a complex kernel by evolutionary means. In order to achieve this purpose we ...

Article history: Received 5 September 2008 Received in revised form 14 July 2010 Accepted 16 July 2010 Available online 30 July 2010