Results 261 to 270 of about 45,420 (307)

Kernel Smoothing

, 1994
Kernel smoothing refers to a general methodology for recovery of underlying structure in data sets. The basic principle is that local averaging or smoothing is performed with respect to a kernel function.
Matt P. Wand, M. Chris Jones
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Kernel covariance series smoothing

2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP), 2015
In this paper, we provide a new viewpoint of sequential random processes of the kind F(x), where x is a multivariate vector of covariates, in terms of a smoothing operation governed by a covariance function. By exploiting the eigenvalues and eigenvectors of the covariance function, we represent the smooth function in terms of an orthogonal series over ...
Cristina Soguero-Ruíz, Robert Jenssen
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Smoothed Bagging with Kernel Bandwidth Selectors

Neural Processing Letters, 2001
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Shinjae Lee, Sungzoon Cho
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On the Smoothness of General Kernels

Canadian Journal of Mathematics, 1966
In (3, §2), the writer and F. E. Browder stated briefly, without proof, some results concerning general distribution kernels. It is our aim here to prove and complete those results.The terminology and notations are introduced in §1.In §2 we define the notion of domain of dependence with respect to the kernel Kx,y (Definition 1) as well as the notion of
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Efficient Density Evaluation for Smooth Kernels

2018 IEEE 59th Annual Symposium on Foundations of Computer Science (FOCS), 2018
Given a kernel function k(.,.) and a dataset P⊂ R^d, the kernel density function of P at a point xe R^d is equal to KDF_P(x):= 1/|P| Σ_yeP k(x, y). Kernel density evaluation has numerous applications, in scientific computing, statistics, computer vision, machine learning and other fields.
Arturs Backurs, Moses Charikar, Piotr Indyk, Paris Siminelakis   +3 more
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Smooth Bayesian Kernel Machines

2005
In this paper, we consider the possibility of obtaining a kernel machine that is sparse in feature space and smooth in output space. Smooth in output space implies that the underlying function is supposed to have continuous derivatives up to some order.
Rutger W. ter Borg, Léon J. M. Rothkrantz   +1 more
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Kernel smoothing for jagged edge reduction

2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013
In this paper, we consider the problem of removing jaggy artifacts from images. We consider the kernel regression framework and propose a reduced-rank quadratic adaptive method that adapts to the local gradient direction. The proposed technique is effective in shrinking isophote fluctuations, and the result is smooth edges.
Mohammad Aghagolzadeh, Andrew Segall
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KERNEL REGRESSION SMOOTHING OF TIME SERIES

Journal of Time Series Analysis, 1992
Abstract. A class of non‐parametric regression smoothers for times series is defined by the kernel method. The kernel approach allows flexible modelling of a time series without reference to a specific parametric class. The technique is applicable to detection of non‐linear dependences in time series and to prediction in smooth regression models with ...
Härdle, Wolfgang, Vieu, Philippe
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Smoothing for Discrete Kernels in Discrimination

Biometrical Journal, 1988
AbstractIn multivariate discrimination by the discrete kernel method the allocation rule is Bayes risk consistent if the smoothing parameter is chosen by maximization of the leaving‐one‐out nonerror rate. It is shown that consistency still holds if the leaving‐one‐out nonerror rate is replaced by a smoothed version. Thus a cross‐validatory criterion is
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Eigenvalues of smooth kernels

Mathematical Proceedings of the Cambridge Philosophical Society, 1984
Suppose is a symmetric square integrable kernel on the unit square [0, 1]2. Thenis a compact symmetric operator on the Hilbert space L2[0, 1]. H. Weyl (see [2]) has shown that, if then the eigenvaluesof T satisfy as n → ∞. We prove a related result.
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