Results 251 to 260 of about 665,590 (295)

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
openaire   +1 more source

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
openaire   +2 more sources

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
openaire   +1 more source

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
openaire   +1 more source

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
openaire   +1 more source

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.
openaire   +2 more sources

A note on the behaviour of a kernel-smoothed kernel density estimator

Statistics & Probability Letters, 2020
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Janssen, Paul, Swanepoel, Jan, Veraverbeke, Noël   +2 more
openaire   +1 more source

Fast Kernel Smoothing by a Low-Rank Approximation of the Kernel Toeplitz Matrix

Journal of Mathematical Imaging and Vision, 2018
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Guang Deng, Jonathan H. Manton, Song Wang 0003   +2 more
openaire   +3 more sources

Evolving Smoothing Kernels for Global Optimization

2016
The Diffusion-Equation Method (DEM) – sometimes synonymously called the Continuation Method – is a well-known natural computation approach in optimization. The DEM continuously transforms the objective function by a (Gaussian) kernel technique to reduce barriers separating local and global minima.
Paul Manns, Kay Hamacher
openaire   +1 more source

Adapting kernel estimation to uncertain smoothness [PDF]

, 2011
For local and average kernel based estimators, smoothness conditions ensure that the kernel order determines the rate at which the bias of the estimator goes to zero and thus allows the econometrician to control the rate of convergence. In practice, even with smoothness the estimation errors may be substantial and sensitive to the choice of the ...
Yulia Kotlyarova, Marcia M Schafgans, Victoria Zinde-Walsh   +2 more
openaire   +1 more source