Commercial detection in news broadcast videos involves judicious selection of meaningful audio-visual feature combinations and efficient classifiers. And, this problem becomes much simpler if these combinations can be learned from the data. To this end, we propose an Multiple Kernel Learning based method for boosting successful kernel functions while ignoring the irrelevant ones. We adopt a int...

The efficiency and performance of the Twin Support Vector Machines (TWSVM) are better than the traditional support vector machines when it deals with the problems. However, it also has the problem of selecting kernel functions. Generally, TWSVM selects the Gaussian radial basis kernel function. Although it has a strong learning ability, its generalization ability is relatively weak. In a certai...

In this study a numerical method is developed to solve the Hammerstein integral equations. To this end the kernel has been approximated using the leastsquares approximation schemes based on Legender-Bernstein basis. The Legender polynomials are orthogonal and these properties improve the accuracy of the approximations. Also the nonlinear unknown function has been approximated by using the Berns...

This article studies the convolutional kernel function of fractal operators in bone fibers. On basis micro-nano composite structure compact bone, we abstracted physical space fibers and derived operators. The aims to construct analytical expression proves that error is core component convolution In other words, mechanics fractional controlled by function.

In kernel-based learning algorithms the kernel function determines the scalar product and thereby the metric in the feature space in which the learning algorithm operates. The kernel is usually not adapted by the kernel method itself. Choosing the right kernel function is crucial for the training accuracy and generalization capabilities of the learning machine. It may also influence the runtime...

This paper presents approximate confidence intervals for each function of parameters in a Banach space based on a bootstrap algorithm. We apply kernel density approach to estimate the persistence landscape. In addition, we evaluate the quality distribution function estimator of random variables using integrated mean square error (IMSE). The results of simulation studies show a significant impro...

Support vector machines and other kernel methods have successfully been applied to various tasks in attribute-value learning. A kernel function is any function in input space that corresponds to an inner product in some feature space. In this discussion paper, we propose a kernel function on strongly typed first-order terms, and we show how this kernel corresponds to the linear inner product in...

Kernel methods implicitly map data points from the input space to some feature space where even relatively simple algorithms such as linear methods can deliver very impressive performance. Of crucial importance though is the choice of the kernel function, which determines the mapping between the input space and the feature space. The past few years have seen many efforts in learning either the ...

We aimed to derive a kernel function that accounts for the interaction among moving particles within the framework of particle method. To predict a computationally more accurate moving particle solution for the Navier-Stokes equations, kernel function is a key to success in the development of interaction model. Since the smoothed quantity of a scalar or a vector at a spatial location is mathema...