Results 91 to 100 of about 16,578 (196)
Learnability in Hilbert Spaces with Reproducing Kernels
Journal of Complexity, 2002 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Uniform Distribution, Discrepancy, and Reproducing Kernel Hilbert Spaces
Journal of Complexity, 2001 The results are related with numerical integration of functions in a reproducing kernel Hilbert space (RKHS). The authors define a notion of uniform distribution and discrepancy of sequences in an abstract set \(E\) in terms of a RKHS of functions on \(E\). In the case of the finite-dimensional unit cube the discrepancies introduced are closely related
Amstler, Clemens, Zinterhof, Peter
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Nonparametric maximum likelihood estimation of probability densities by penalty function methods [PDF]
When it is known a priori exactly to which finite dimensional manifold the probability density function gives rise to a set of samples, the parametric maximum likelihood estimation procedure leads to poor estimates and is unstable; while the ...
Demontricher, G. F. +2 more
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Radial kernels and their reproducing kernel Hilbert spaces
Journal of Complexity, 2010 Let \(R\) be a continuous convex function on a Hilbert space \(H\). In learning theory, \[ A(\lambda):= \inf_{h\in H} \{\lambda\| h\|^2+ R(h)\}- \inf_{h\in H} R(h) \] is called an approximation error function. Here, \(H\) is a reproducing kernel Hilbert space (RKHS) of functions on \(\mathbb{R}^d\), i.e., such that the evaluations \(\delta_x: h\mapsto ...
Scovel, Clint +3 more
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A Characterization for reproducing kernel Hilbert spaces
Journal of Mathematical Analysis and Applications, 1974 AbstractLet G(t, s) be the Green's functions associated with N, a differential operator restricted to certain boundary conditions. Define (u, v)N = (Nu, v)L2. It is shown that the reproducing kernel Hilbert space generated by G is the same as the Hilbert-space completion with respect to ∥ · ∥N of the set of real valued functions which are in C2n and ...
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Flexible Expectile Regression in Reproducing Kernel Hilbert Spaces
Technometrics, 2017 Expectile, first introduced by Newey and Powell in 1987 in the econometrics literature, has recently become increasingly popular in risk management and capital allocation for financial institutions due to its desirable properties such as coherence and elicitability.
Yang, Yi, Zhang, Teng, Zou, Hui
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Machine Learning Based System Identification with Binary Output Data Using Kernel Methods
Journal of Telecommunications and Information TechnologyWithin the realm of machine learning, kernel methods stand out as a prominent class of algorithms with widespread applications, including but not limited to classification, regression, and identification tasks.
Rachid Fateh +7 more
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A Kernel-Based Metric for Balance Assessment
Journal of Causal Inference, 2018 An important goal in causal inference is to achieve balance in the covariates among the treatment groups. In this article, we introduce the concept of distributional balance preserving which requires the distribution of the covariates to be the same in ...
Zhu Yeying +2 more
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Applied SciencesSoftware-defined networking (SDN) requires adaptive control strategies to handle dynamic traffic conditions and heterogeneous network environments.
Yedil Nurakhov +3 more
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Some Lemmas on Reproducing Kernel Hilbert Spaces [PDF]
, 2002 Reproducing kernel Hilbert spaces (RKHS) provides a framework for approximation from finite data using the idea of bounded linear functionals. The approximation problem in this case can be viewed as the inverse problem of finding the optimum operator from the Euclidean space of observations to some subspace of the RKHS.
Dodd, T.J., Harrison, R.F.
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