Kernel methods, being supported by a well-developed theory and coming with efficient algorithms, are among the most popular successful machine learning techniques. From mathematical point of view, these methods rest on concept kernels function spaces generated kernels, so–called reproducing kernel Hilbert spaces. Motivated recent developments approaches in context interacting particle systems, ...

The kernel least mean squares (KLMS) algorithm is a computationally efficient nonlinear adaptive filtering method that “kernelizes” the celebrated (linear) least mean squares algorithm. We demonstrate that the least mean squares algorithm is closely related to the Kalman filtering, and thus, the KLMS can be interpreted as an approximate Bayesian filtering method. This allows us to systematicall...

In this paper, we study the minimax estimation of the Bochner integral μk(P ) := ∫

For analytic functions the remainder term of Gaussian quadrature rules can be expressed as a contour integral with kernel Kn. In this paper the kernel is studied on elliptic contours for a great variety of symmetric weight functions including especially Gegenbauer weight functions. First a new series representation of the kernel is developed and analyzed. Then the location of the maximum modulu...

A mean function in reproducing kernel Hilbert space, or a kernel mean, is an important part of many applications ranging from kernel principal component analysis to Hilbert-space embedding of distributions. Given finite samples, an empirical average is the standard estimate for the true kernel mean. We show that this estimator can be improved via a well-known phenomenon in statistics called Ste...

Face recognition techniques have gained much attention and research interests over the recent years due to their vast applications in security and authentication systems. Some of the popular approaches involve support vector machines (SVM), which can either be a binary or a multiclass classification problem, and subspace learning, where data is assumed to lie on some low dimensional manifold, s...

reported grain yield increased 3.9 and 7.4% cycle 1, respectively, over two cycles of selection in barley Increasing kernel weight has been proposed as a method to increase (Hordeum vulgare L.). Durum wheat (Triticum turflour extraction in spring wheat (Triticum aestivum L.). Recurrent selection was initiated to increase kernel weight while maintaining gidum L. var. durum) grain yield improved ...

mihan is a new bread wheat cultivar originated from the cross between iranian cultivar barakat with the chinese cultivar 90-zhong 87 at karaj research station in 1995-96 cropping season. the f1 plants were grown at karaj in 1996-97 cropping season, furthermore f2-f6 populations and regional preliminary yield nursery were evaluated at ardebil agricultural research station during 1997-2003 croppi...

A mean function in a reproducing kernel Hilbert space (RKHS), or a kernel mean, is an important part of many algorithms ranging from kernel principal component analysis to Hilbert-space embedding of distributions. Given a finite sample, an empirical average is the standard estimate for the true kernel mean. We show that this estimator can be improved due to a well-known phenomenon in statistics...