Results 231 to 240 of about 8,931 (261)

The Kernel Rough K-Means Algorithm

Recent Advances in Computer Science and Communications, 2020
Background: Clustering is one of the most important data mining methods. The k-means (c-means ) and its derivative methods are the hotspot in the field of clustering research in recent years. The clustering method can be divided into two categories according to the uncertainty, which are hard clustering and soft clustering. The Hard C-Means clustering
Wang Meng, Dui Hongyan, Zhou Shiyuan, Dong Zhankui, Wu Zige   +4 more
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Parabolic Littlewood-Paley g-function with rough kernel

Acta Mathematica Sinica, English Series, 2008
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yong Ding
exaly   +2 more sources

A rough fuzzy kernel clustering algorithm

2015 IEEE International Conference on Communication Problem-Solving (ICCP), 2015
Traditional clustering algorithm can't deal with non-linear fuzzy and boundary problem. This paper provides a rough fuzzy kernel clustering algorithm. The algorithm firstly using kernel function map input space to high-dimensional space, make the space can be partitioned linearly.
Ouyang Hao, Feng Wang Ri, Wen Wang Zhi, Jin Huang Zhen   +3 more
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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 0009, Huiling Lu, Deren Yang, Jingxian Ma, Shouheng Tuo   +4 more
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Rough Cluster Algorithm Based on Kernel Function

2008
By means of analyzing kernel clustering algorithm and rough set theory, a novel clustering algorithm, rough kernel k-means clustering algorithm, was proposed for clustering analysis. Through using Mercer kernel functions, samples in the original space were mapped into a highdimensional feature space, which the difference among these samples in sample ...
Tao Zhou 0009, Yanning Zhang 0001, Huiling Lu, Fang'an Deng, Fengxiao Wang   +4 more
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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 large
Raia Hadsell, J. Andrew Bagnell, Daniel F. Huber, Martial Hebert   +3 more
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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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On Singular Integral Operators with Rough Kernel Along Surface

Integral Equations and Operator Theory, 2010
Let \(\Omega\) be a homogeneous function of degree 0 on \(\mathbb{R}^n\), with \(\Omega \in L^1(S^{n-1})\) and \( \int \Omega (y') d\sigma (y')=0. \) Suppose that \(\Phi\) is a nonnegative (or nonpositive) and monotonic \(C^1\) function on \((0, \infty)\) such that \( \varphi (t) := \frac{\Phi(t)}{t \Phi'(t)} \) is bounded.
Ding, Yong, Xue, Qingying, Yabuta, Kôzô   +2 more
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Rough Sets, Kernel Set, and Spatiotemporal Outlier Detection

IEEE Transactions on Knowledge and Data Engineering, 2014
Nowadays, the high availability of data gathered from wireless sensor networks and telecommunication systems has drawn the attention of researchers on the problem of extracting knowledge from spatiotemporal data. Detecting outliers which are grossly different from or inconsistent with the remaining spatiotemporal data set is a major challenge in real ...
Alfredo Petrosino, Sankar K. Pal, Alessia Albanese   +2 more
exaly   +4 more sources

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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