Kernel independent component analysis (KICA) is a blind signal separation method which has a good effect for the treatment of non-linear signal. For introducing kernel techniques, the choices of kernel function and its kernel parameter have a great influence on the analytic results. A kernel function and its parameters optimization method is proposed on the basis of the similarity of source fau...

A function on R with multiple local minima is approximated from below, via linear programming, by a linear combination of convex kernel functions using sample points from the given function. The resulting convex kernel underestimator is then minimized, using either a linear equation solver for a linear-quadratic kernel or by a Newton method for a Gaussian kernel, to obtain an approximation to a...

This paper concentrates on a new procedure which experimentally recognises gears and bearings faults of a typical gearbox system using a least square support vector machine (LSSVM). Two wavelet selection criteria Maximum Energy to Shannon Entropy ratio and Maximum Relative Wavelet Energy are used and compared to select an appropriate wavelet for feature extraction. The fault diagnosis method co...

In this paper, we present a method to enhance noisy depth maps using adaptive steering kernel regression based on distance transform. Dataadaptive kernel regression filters are widely used for image denoising by considering spatial and photometric properties of pixel data. In order to reduce noise in depth maps more efficiently, we adaptively refine the steering kernel regression function accor...

We introduce a new barrier function which has a linear growth term in its kernel function. So far all existing kernel functions have a quadratic (or higher degree) growth term. Despite this, a large-update primal-dual interior-point method based on this kernel function has the same iteration bound as the classical primal-dual method, which is based on the logarithmic barrier method.

In the last few years, application of Support Vector Machines (SVMs) for solving classification and regression problems has increased, in particular, due to its high generalization performance and its ability to model non-linear relationships. The latter can only be realised if a suitable kernel function is applied. This kernel function transforms the non-linear input space into a high dimensio...

Support vector machine (SVM) is one of the popular machine learning techniques used in various text processing tasks including named entity recognition (NER). The performance of the SVM classifier largely depends on the appropriateness of the kernel function. In the last few years a number of task-specific kernel functions have been proposed and used in various text processing tasks, for exampl...

We study the asymptotical properties of indefinite kernel network with coefficient regularization and dependent sampling. The framework under investigation is different from classical kernel learning. Positive definiteness is not required by the kernel function and the samples are allowed to be weakly dependent with the dependence measured by a strong mixing condition. By a new kernel decomposi...