Kernel theory is a demonstrated tool that has made its way into nearly all areas of machine learning. However, serious limitation kernel methods knowing which needed in practice. Multiple learning (MKL) an attempt to learn new tailored through the aggregation set valid known kernels. There are generally three approaches MKL: fixed rules, heuristics, and optimization. Optimization most popular; ...

in this paper, we introduce the notion of an action $y_x$as a generalization of the notion of a module,and the notion of a norm $vt: y_xto f$, where $f$ is a field and $vartriangle(xy)vartriangle(y') =$ $ vartriangle(y)vartriangle(xy')$ as well as the notion of fuzzy norm, where $vt: y_xto [0, 1]subseteq {bf r}$, with $bf r$  the set of all real numbers. a great many standard mappings on algebr...

on the basis of a reproducing kernel space, an iterative algorithm for solving the one-dimensional linear and nonlinear schrödinger equations is presented. the analytical solution is shown in a series form in the reproducing kernel space and the approximate solution is constructed by truncating the series. the convergence of the approximate solution to the analytical solution is also proved. th...

Discrete wavelet transforms are extensively preferred in biomedical signal processing for denoising, feature extraction, and compression. This paper presents a new denoising method based on the modeling of discrete wavelet coefficients of ECG in selected sub-bands with Kernel density estimation. The modeling provides a statistical distribution of information and noise. A Gaussian kernel with bo...

Kernel density estimators are the basic tools for density estimation in non-parametric statistics.  The k-nearest neighbor kernel estimators represent a special form of kernel density estimators, in  which  the  bandwidth  is varied depending on the location of the sample points. In this paper‎, we  initially introduce the k-nearest neighbor kernel density estimator in the random left-truncatio...

In this paper, Gaussian Process Regression (GPR)-based models which use the Bayesian approach to regression analysis problem such as load forecasting (LF) are proposed. The GPR is a non-parametric kernel-based learning method having ability provide correct predictions with uncertainty in measurements. proposed model provides an hourly and monthly forecast for Australian city four Indian cities ...

Traits related to nitrogen fixation may be used as indirect selection criteria foraflatoxin resistance in peanut. The aim of this study was to investigate therelationship between N2 fixation traits and aflatoxin contamination in peanut underdifferent drought conditions. Eleven peanut genotypes were evaluated under threewater regimes for two seasons in the field. Data were observed on kernel inf...