kernel hilbert space
A Brief Digest on Reproducing Kernel Hilbert Space
... Reproducing Kernel Hilbert Space (RKHS) is a common used tool in statistics and machine learning to generalize from linear models to non-linear ...given kernel function. This view is highly ...
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Step Size Adaptation in Reproducing Kernel Hilbert Space
... reproducing kernel Hilbert space (RKHS) and translate SMD to the nonparametric setting, where its gradient trace parameter is no longer a coefficient vector but an element of the ...
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Solving multi-order fractional differential equations by reproducing kernel Hilbert space method
... Abstract In this paper, we propose a relatively new semi-analytical technique to approximate the solution of nonlinear multi-order fractional differential equations (FDEs). We present some results concerning to the ...
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Model-free Variable Selection in Reproducing Kernel Hilbert Space
... In this article, we propose a novel model-free variable selection method, which requires no explicit model assumptions and allows for general variable effects. The method is based on the idea that a variable is truly ...
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Reproducing Kernel Hilbert space regression with notes on the Wasserstein Distance
... reproducing-kernel Hilbert space (RKHS) has been extensively studied (Smale and Zhou, 2007; Caponnetto and de Vito, 2007; Steinwart and Christmann, 2008; Mendelson and Neeman, 2010; Steinwart et ...
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Application of reproducing kernel Hilbert space method for solving second order fuzzy Volterra integro differential equations
... Hilbert space. This method illustrates the ability of the reproducing kernel concept of the Hilbert space to approximate the solutions of second-order fuzzy Volterra integro-differential ...
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Kernel Partial Least Squares Regression in Reproducing Kernel Hilbert Space
... Classical PCR, PLS and RR techniques are well known shrinkage estimators designed to deal with multicollinearity (see, e.g., Frank and Friedman, 1993, Montgomery and Peck, 1992, Jolliffe, 1986). The multicollinearity or ...
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Solving singular second-orderinitial/boundary value problems in reproducing kernel Hilbert space
... According to our method, which is presented in this paper, some reproducing kernel Hilbert spaces have been presented in the first step. And in the second step, the homo- geneous IBVPs is deal with in the ...
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Solving an inverse problem for a parabolic equation with a nonlocal boundary condition in the reproducing kernel space
... reproducing kernel Hilbert space method was applied suc- cessfully for solving an inverse problem for a parabolic equation with nonlocal boundary ...
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Reconstruction of Sensory Stimuli Encoded with Integrate-and-Fire Neurons with Random Thresholds
... We present a general approach to the reconstruction of sensory stimuli encoded with leaky integrate-and-fire neurons with random thresholds. The stimuli are modeled as elements of a Reproducing Kernel ...
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Approximate solutions for MHD squeezing fluid flow by a novel method
... reproducing kernel Hilbert space method (RKHSM) has been implemented to obtain a solution of the reduced fourth-order nonlinear boundary value ...
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Generalisations of Pick's theorem to reproducing Kernel Hilbert spaces
... the space of multipliers of a general reproducing kernel Hilbert space H(K) (where K is the reproducing ...Sobolev space or the Dirichlet ...reproducing kernel Hilbert ...
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Universality, Characteristic Kernels and RKHS Embedding of Measures
... by kernel-based classification/regression algo- rithms while characteristic kernels are introduced in the context of distinguishing probability mea- sures by embedding them into a reproducing kernel ...
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Refinable Kernels
... The main purpose of this paper is to introduce the notion of refinable kernels, wavelet-like reproduc- ing kernels and multiresolution analysis of a reproducing kernel Hilbert space. Before ...
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Kernel Mean Shrinkage Estimators
... the kernel mean µ in a reproducing kernel Hilbert space H and showed they improve upon the empirical estimator ˆ µ in the mean squared ...
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RECKONING OF PRISTINE SIGNAL AND MELIORATING ALGORITHM CONSTANCY BY OVERCOMING AMBIGUITY
... Blind Source Separation is performed by using Reproductive Kernel Hilbert Space which makes use of contrast function on canonical correlation, by which higher order statistics can be reckoned. The ...
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Synergy of Monotonic Rules
... In order to construct a monotonic solution, we use the set of functions that belong to Reproducing Kernel Hilbert Space (RKHS) associated with the INK-spline kernel (splines with Infinite ...
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Differential Privacy for Functions and Functional Data
... reproducing kernel Hilbert space (RKHS) generated by the covariance kernel of the Gaussian process, then the correct noise level is established by measur- ing the “sensitivity” of the function ...
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A Refined MCMC Sampling from RKHS for PAC-Bayes Bound Calculation
... concept space. In this paper, by formulating the concept space as Reproducing Kernel Hilbert Space (RKHS) using the kernel method, we proposed a refined Markov Chain Monte Carlo ...
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New Implementation of Reproducing Kernel Method for Solving Functional Differential Equations
... 2009 Solving Singular Second Order Three-Point Boundary Value Problems Using Reproducing Kernel Hilbert Space Method.. 2012 A Numerical Solution to Nonlinear Second Order Three-Point Bou[r] ...
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