Results 251 to 260 of about 102,807 (280)

On Marcinkiewicz Integral Operators with Rough Kernels

Integral Equations and Operator Theory, 2005
This paper is devoted to the study on the L p -mapping properties of Marcinkiewicz integral operators with rough kernels along “polynomial curves” on $$\mathbb{R}^n .$$ The
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Singular Integrals along Surfaces of Revolution with Rough Kernels

SUT Journal of Mathematics, 2003
Let \(n\geq 2\) and \(S^{n-1}\) be the unit sphere in \(\mathbb{R}^n\) equipped with the normalized Lebesgue measure \(d\sigma\). Suppose that \(\Omega\) is a homogeneous function of degree zero on \(\mathbb{R}^n\) that satisfies \(\Omega\in B_q^{0,0}(S^{n-1})\) and \(\int_{S^{n-1}} \Omega (x)d\sigma=0\), where \(B_q^{0,0}\) is a certain block space ...
Al-Qassem, Hussain, Pan, Yibiao
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A roughness-penalty view of kernel smoothing

Statistics & Probability Letters, 2001
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Applications of Boolean Kernels in Rough Sets

2014
Rough Sets (RS) and Support Vector Machine (SVM) are the two big and independent research areas in AI. Originally, rough set theory is dealing with the concept approximation problem under uncertainty. The basic idea of RS is related to lower and upper approximations, and it can be applied in classification problem.
Sinh Hoa Nguyen, Hung Son Nguyen
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Parabolic singular integrals with rough kernels and extrapolation

SCIENTIA SINICA Mathematica, 2014
This paper is devoted to studying the parabolic singular integrals with rough kernels both on the unit sphere and in the radial direction, as well as the corresponding maximal singular integrals. By the estimates of Fourier transforms, the Littlewood-Paley theory and the extrapolation arguments, under the rather weak size conditions, which are the best
Feng LIU, HuoXiong WU
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Rough Kernel Clustering Algorithm with Adaptive Parameters

2011
Through analyzing kernel clustering algorithm and rough set theory, a novel clustering algorithm, Rough kernel k-means clustering algorithm with adaptive parameters, is proposed for clustering analysis in this paper. By using Mercer kernel functions, we can map the data in the original space to a highdimensional feature space, in which we can use rough
Tao Zhou, Huiling Lu, Deren Yang, Jingxian Ma, Shouheng Tuo   +4 more
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Kernel based K-means clustering using rough set

2012 International Conference on Computer Communication and Informatics, 2012
From the beginning of the data analysis system cluster computing plays an important role on it. The very early developed clustering algorithms which can handle only numerical data and K-means clustering is one of them and was proposed by Macqueen [1] in 1967. This algorithm helps us to find the homogeneity of the data set.
B. K. Tripathy, Adhir Ghosh, G. K. Panda
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Space-carving Kernels for Accurate Rough Terrain Estimation

The International Journal of Robotics Research, 2010
Accurate terrain estimation is critical for autonomous offroad navigation. Reconstruction of a three-dimensional (3D) surface allows rough and hilly ground to be represented, yielding faster driving and better planning and control. However, data from a 3D sensor samples the terrain unevenly, quickly becoming sparse at longer ranges and containing ...
Raia Hadsell, J. Andrew Bagnell, Daniel Huber, Martial Hebert   +3 more
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An extension of Marcinkiewicz integrals with rough kernels

Acta Mathematica Scientia
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wu, Huoxiong, Wu, Lin
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Rough estimates on the scalar heat kernel

2011
This chapter establishes rough estimates on the heat kernel rb,tX for the scalar hypoelliptic operator AbX on X defined in the preceding chapter. By rough estimates, this chapter refers to just the uniform bounds on the heat kernel. The chapter also obtains corresponding bounds for the heat kernels associated with operators AbX and another AbX over ̂X.
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