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 class of polynomial-time primal-dual interior-point methods (IPMs) for semide nite optimization based on a new class of kernel functions. This class is fairly general and includes the class of nite kernel functions [1]: the corresponding barrier functions have a nite value at the boundary of the feasible region. They are not exponentially convex and also not strongly ...

In support vector classi er, asymmetric kernel functions are not used so far, although they are frequently used in other kernel classi ers. The applicable kernels are limited to symmetric semipositive de nite ones because of Mercer's theorem. In this paper, SVM is extended to be applicable to asymmetric kernel functions. It is proven that, when a positive de nite kernel is given, the extended S...

The class of symmetric function interacts extensively with other types functions. One these is the convex functions, which closely related to theory symmetry. In this paper, we obtain some new fractional Hermite–Hadamard inequalities an exponential kernel for subadditive functions and their product, known results are recaptured. Moreover, using a identity as auxiliary result, deduce several per...

Differential privacy is a framework for privately releasing summaries of a database. Previous work has focused mainly on methods for which the output is a finite dimensional vector, or an element of some discrete set. We develop methods for releasing functions while preserving differential privacy. Specifically, we show that adding an appropriate Gaussian process to the function of interest yie...

We consider conditions on a given system F of vectors in Hilbert space H, forming a frame, which turn H into a reproducing kernel Hilbert space. It is assumed that the vectors in F are functions on some set Ω. We then identify conditions on these functions which automatically give H the structure of a reproducing kernel Hilbert space of functions on Ω. We further give an explicit formula for th...

In this paper we propose a novel evolutionary algorithm for regularization networks. The main drawback of regularization networks in practical applications is the presence of meta-parameters, including the type and parameters of kernel functions Our learning algorithm provides a solution to this problem by searching through a space of different kernel functions, including sum and composite kern...

In this paper, we discuss some equivalences between two recently introduced statistical learning schemes, namely Mercer kernel methods and information theoretic methods. We show that Parzen window-based estimators for some information theoretic cost functions are also cost functions in a corresponding Mercer kernel space. The Mercer kernel is directly related to the Parzen window. Furthermore, ...

Kernel machines as well as neural networks possess universal function approximation properties. Nevertheless in practice their ways of choosing the appropriate function class differ. Specifically neural networks learn a representation by adapting their basis functions to the data and the task at hand, while kernel methods typically use a basis that is not adapted during training. In this work, ...

We show how to characterize the energy consumption of individual operating system (OS) functions in the mC/OS-II real time kernel running on an ARM7TDMI-based embedded system. We then derive a strategy for saving energy based on locking more energy-consuming kernel routines of mC/OS-II into the cache and reassigning cache locations to reduce cache contention between frequently invoked kernel fu...