Results 11 to 20 of about 1,638 (210)
Single image super-resolution via an iterative reproducing kernel Hilbert space method. [PDF]
IEEE Trans Circuits Syst Video Technol, 2016 Image super-resolution, a process to enhance image resolution, has important applications in satellite imaging, high definition television, medical imaging, etc. Many existing approaches use multiple low-resolution images to recover one high-resolution image. In this paper, we present an iterative scheme to solve single image super-resolution problems.
Deng LJ, Guo W, Huang TZ.
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Iterative Multistep Reproducing Kernel Hilbert Space Method for Solving Strongly Nonlinear Oscillators [PDF]
Advances in Mathematical Physics, 2014 A new algorithm called multistep reproducing kernel Hilbert space method is represented to solve nonlinear oscillator’s models. The proposed scheme is a modification of the reproducing kernel Hilbert space method, which will increase the intervals of ...
Banan Maayah +3 more
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Aveiro method in reproducing kernel Hilbert spaces under complete dictionary [PDF]
Mathematical Methods in the Applied Sciences, 2017 Aveiro method is a sparse representation method in reproducing kernel Hilbert spaces, which gives orthogonal projections in linear combinations of reproducing kernels over uniqueness sets. It, however, suffers from determination of uniqueness sets in the underlying reproducing kernel Hilbert space.
Weixiong Mai, Tao Qian
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A reproducing kernel Hilbert space approach in meshless collocation method [PDF]
Computational and Applied Mathematics, 2019 In this paper we combine the theory of reproducing kernel Hilbert spaces with the field of collocation methods to solve boundary value problems with special emphasis on reproducing property of kernels. From the reproducing property of kernels we proposed a new efficient algorithm to obtain the cardinal functions of a reproducing kernel Hilbert space ...
Babak Azarnavid +3 more
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Reproducing Kernel Hilbert Spaces Regression and Classification Methods [PDF]
, 2022 AbstractThe fundamentals for Reproducing Kernel Hilbert Spaces (RKHS) regression methods are described in this chapter. We first point out the virtues of RKHS regression methods and why these methods are gaining a lot of acceptance in statistical machine learning.
Osval A. Montesinos‐López +2 more
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The reproducing kernel Hilbert space method for solving Troesch’s problem [PDF]
Journal of the Association of Arab Universities for Basic and Applied Sciences, 2013 AbstractIn this paper, the reproducing kernel Hilbert space method (RKHSM) is applied for solving Troesch’s problem. We used numerical examples to illustrate the accuracy and implementation of the method. The analytical result of the equation has been obtained in terms of a convergent series with easily computable components.
Mustafa İnç, Ali Akguül
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Solutions to Uncertain Volterra Integral Equations by Fitted Reproducing Kernel Hilbert Space Method [PDF]
Journal of Function Spaces, 2016 We present an efficient modern strategy for solving some well-known classes of uncertain integral equations arising in engineering and physics fields. The solution methodology is based on generating an orthogonal basis upon the obtained kernel function ...
Ghaleb Gumah +3 more
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Kernel Center Adaptation in the Reproducing Kernel Hilbert Space Embedding Method [PDF]
International Journal of Adaptive Control and Signal Processing, 2020 SummaryThe performance of adaptive estimators that employ embedding in reproducing kernel Hilbert spaces (RKHS) depends on the choice of the location of basis kernel centers. Parameter convergence and error approximation rates depend on where and how the kernel centers are distributed in the state‐space.
Sai Tej Paruchuri +2 more
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Soft and hard classification by reproducing kernel Hilbert space methods [PDF]
Proceedings of the National Academy of Sciences, 2002 Reproducing kernel Hilbert space (RKHS) methods provide a unified context for solving a wide variety of statistical modelling and function estimation problems. We consider two such problems: We are given a training set { y i ,
Grace Wahba
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Reproducing kernel Hilbert space methods for modelling the discount curve [PDF]
We consider the theory of bond discounts, defined as the difference between the terminal payoff of the contract and its current price. Working in the setting of finite-dimensional realizations in the HJM framework, under suitable notions of no-arbitrage, the admissible discount curves take the form of polynomial, exponential functions.
Andreas Celary +2 more
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