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18 results with keyword: 'reproducing kernel hilbert space method proportional hazard model'

Reproducing kernel Hilbert space method for cox proportional hazard model

EXTENSION OF COX MODEL WITH MODIFIED REPRODUCING KERNEL HILBERT SPACE 4.1 Introduction 4.2 Kernel Methods 4.2.1 Representer Theorem 4.2.2 Gaussian Processes 4.2.3 Implications for

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2021
Model-free Variable Selection in Reproducing Kernel Hilbert Space

This section examines the effectiveness of the proposed model-free variable selection method, and compares it against some popular model based methods in literature, including

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2020
The Reproducing Kernel Hilbert Space Method for Solving System          of Linear Weakly Singular Volterra Integral Equations

The Reproducing Kernel Hilbert Space Method for Solving System of Linear Weakly Singular Volterra Integral Equations..

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2021
Policy Search in Reproducing Kernel Hilbert Space

We compare our new framework to the recently intro- duced RKHS actor-critic framework (RKHS-AC) [Lever and Stafford, 2015], other parametric approaches such as the episodic

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2022
The reproducing kernel Hilbert space method for solving Troesch’s problem

Results obtained by the method are compared with the exact solution, and each k values of the Adomian decomposition method (ADM) ( Deeba et al., 2000 ), the Laplace decomposition

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2021
Reproducing Kernel Hilbert Space vs. Frame Estimates

Our concern here is to make the connection between the initial Hilbert space H (with frame vectors) and an associated reproducing kernel Hilbert space of functions on Ω.. An

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2021
A Refined MCMC Sampling from RKHS for PAC-Bayes Bound Calculation

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 (MCMC)

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2020
Numerical solution of Troesch's problem using Christov rational functions

Moreover, the modified homotopy perturbation technique [14], differential transform method [7], reproducing kernel Hilbert space method [17] and the Jacobi collocation method [13]

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2020
Solving singular second-orderinitial/boundary value problems in reproducing kernel Hilbert space

Geng, FZ: Solving singular second order three-point boundary value problems using reproducing kernel Hilbert space method.. Appl

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2020
Verification of Stochastic Reach-Avoid Using RKHS Embeddings

We propose a method for stochastic reachability analysis based on conditional distribution embeddings within a reproducing kernel Hilbert space (RKHS).. Kernel methods are

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2021
Solution of Fractional Mathieu Equation by Reproducing Kernel Hilbert Space Method

Abstract: In this paper we use reproducing kernel Hilbert space method (RKHSM) and fractional power series to solve fractional Mathieu equation.. A comparison tables

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2021
Density Estimation in Infinite Dimensional Exponential Families

Keywords: density estimation, exponential family, Fisher divergence, kernel density estimator, maximum likelihood, interpolation space, inverse problem, reproducing kernel

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Solving multi-order fractional differential equations by reproducing kernel Hilbert space method

As a result, a number of methods have been proposed and applied success- fully to approximate fractional differential equations, such as Adomian decomposition method [17],

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Functional inverse regression and reproducing kernel Hilbert space

Reproducing Kernel Hilbert Space (RKHS) is an effective and profound device for statistical analysis involving infinite dimensional data objects, while inverse regression (IR) is

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Minimax and Adaptive Prediction for Functional Linear Regression

Keywords: Adaptive prediction, functional linear model, minimax rate of convergence, principal components analysis, reproducing kernel Hilbert space, spectral decomposition..

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Kernel Partial Least Squares Regression in Reproducing Kernel Hilbert Space

A family of regularized least squares regression models in a Reproducing Kernel Hilbert Space is extended by the kernel partial least squares (PLS) regression model.. Similar

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Regularization networks with indefinite kernels

Keywords: Regularization kernel network; Indefinite kernel; Coefficient regularization; Least square regression; Reproducing kernel Hilbert space; Capacity independent error

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Step Size Adaptation in Reproducing Kernel Hilbert Space

In experiments online SVMD outperformed the conventional online SVM (aka NORMA) algorithm with scheduled step size decay for binary and multiclass classification, especially on

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