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...

Combined with weight of samples and kernel function, fuzzy clustering method with generalized entropy is studied. Objective function for fuzzy clustering with generalized entropy based on sample weighting is obtained. Following that, fuzzy clustering algorithm with generalized entropy based on sample weighting is presented. In addition, by introducing kernel into the presented objective functio...

In geotechnical engineering, rock mechanics and engineering geology, depending on the project design, uniaxial strength and static Youngchr('39')s modulus of rocks are of vital importance. The direct determination of the aforementioned parameters in the laboratory, however, requires intact and high-quality cores and preparation of their specimens have some limitations. Moreover, performing thes...

With the introduction of kernel integrity checking mechanisms in modern operating systems, such as PatchGuard on Windows OS, malware developers can no longer easily install stealthy hooks in kernel code and well-known data structures. Instead, they must target other areas of the kernel, such as the heap, which stores a large number of function pointers that are potentially prone to malicious ex...

In this paper an adaptive wavelet kernel based on density SVM approach for P2P traffic classification is presented. The model combines the multi-scale learning ability of wavelet kernel and the advantages of support vector machine. Mexican hat wavelet function is used to build SVM kernel function. The wavelet kernel function is tuned adaptively according to the density of samples around support...

The choice of kernel is essential in kernel-based leaning methods. One way to choose a suitable kernel is cross-validation, but there is an alternative solution, that is, learning the kernel from data. In this project, two papers about learning the kernel matrix and kernel function from data are surveyed and compared. The method of learning the kernel matrix is implemented, and empirical result...

Based on a new parametric kernel function, this paper presents a primaldual large-update interior-point algorithm (IPM) for semi-definite optimization (SDO) problems. The new parametric function is neither self-regular function nor the usual logarithmic barrier function. It is strongly convex and possesses some novel analytic properties. We analyse this new parametric kernel function and show t...

In this paper, we introduce a new kernel function called polynomial radial basis function (PRBF) that could improve the classification accuracy of support vector machines (SVMs). The proposed kernel function combines both Gauss (RBF) and Polynomial (POLY) kernels and is stated in general form. It is shown that the proposed kernel converges faster than the Gauss and Polynomial kernels. The accur...

ll rights reserved. Recently the Chebyshev kernel has been proposed for SVM and it has been proven that it is a valid kernel for scalar valued inputs in [11]. However in pattern recognition, many applications require multidimensional vector inputs. Therefore there is a need to extend the previous work onto vector inputs. In [11], although it is not stated explicitly, the authors recommend evalu...