18 results with keyword: 'reproducing kernel hilbert space method 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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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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The Reproducing Kernel Hilbert Space Method for Solving System of Linear Weakly Singular Volterra Integral Equations..
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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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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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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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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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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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Geng, FZ: Solving singular second order three-point boundary value problems using reproducing kernel Hilbert space method.. Appl
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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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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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Keywords: density estimation, exponential family, Fisher divergence, kernel density estimator, maximum likelihood, interpolation space, inverse problem, reproducing kernel
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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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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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Keywords: Adaptive prediction, functional linear model, minimax rate of convergence, principal components analysis, reproducing kernel Hilbert space, spectral decomposition..
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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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Keywords: Regularization kernel network; Indefinite kernel; Coefficient regularization; Least square regression; Reproducing kernel Hilbert space; Capacity independent error
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