As is well-known, a heteroskedasticity and autocorrelation consistent covariance matrix is proportional to a spectral density matrix at frequency zero and can be consistently estimated by such popular kernel methods as those of Andrews-Newey-West. In practice, it is di¢cult to estimate the spectral density matrix if it has a peak at frequency zero, which can arise when there is strong autocorre...

This paper addresses the problem of optimal feature extraction from a wavelet representation. Our work aims at building features by selecting wavelet coefficients resulting from signal or image decomposition on an adapted wavelet basis. For this purpose, we jointly learn in a kernelized large-margin context the wavelet shape as well as the appropriate scale and translation of the wavelets, henc...

Many practical engineering applications require the usage of accurate automatic decision systems, usually operating under tight computational constraints. Support Vector Machines (SVMs) endowed with a Radial Basis Function (RBF) as kernel are broadly accepted as the current state of the art for decision problems, but require cross-validation to select the free parameters, which is computational...

243  Abstract: Accurate automatic detection and classification of images is very challenging task whether they are medical images or other natural images. This paper presents a hybrid technique for automatic classification of MRI images as well as natural images. The proposed method consists of two stages: feature extraction and classification. In first stage, features are extracted from image...

In this paper we first introduce the Gabor Wavelet Network (GWN) as a model-based approach for effective and efficient object representation. GWNs combine the advantages of the continuous wavelet transform with RBF networks. They have additional advantages such as invariance to some degree with respect to affine deformations. The use of Gabor filters enables the coding of geometrical and textur...

it is important to reduce keeping costs and hold up unscheduled downtimes for machinery. So knowledge of what, where and how faults occur is very important. In machine rotation and machine learning Fault diagnosis and detection are important rule. In this paper offer a method based on kernel method that using in fault occur. For this reason create kernel by wavelet packet with associate rule mi...

This paper explores the Support Vector Machine and Least Square Support Vector Machine models in stock forecasting. Three prevailing forecasting techniques General Autoregressive Conditional Heteroskedasticity (GARCH), Support Vector Regression (SVR) and Least Square Support Vector Machine (LSSVM) are combined with the wavelet kernel to form three novel algorithms Wavelet-based GARCH (WL_GARCH)...

The proposed method is to recognize objects based on application of Local Steering Kernels (LSK) as Descriptors to the image patches. In order to represent the local properties of the images, patch is to be extracted where the variations occur in an image. To find the interest point, Wavelet based Salient Point detector is used. Local Steering Kernel is then applied to the resultant pixels, in ...

Early fungal infection of citrus is one the common diseases found during storage period citrus, and fungus that infects will spread to entire batch as degree deepens, causing enormous economic losses. Therefore, early detection fundamental. The purpose this study explore qualitative identification infections in by using Fourier transform near-infrared (FT-NIR) combined with a variety chemometri...