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Multilinear Singular Integrals with Rough Kernel
Acta Mathematica Sinica, English Series, 2003zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Lu, Shan Zhen, Wu, Huo Xiong, Zhang, Pu
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Kernelized Fuzzy Rough Sets and Their Applications
IEEE Transactions on Knowledge and Data Engineering, 2011Kernel machines and rough sets are two classes of commonly exploited learning techniques. Kernel machines enhance traditional learning algorithms by bringing opportunities to deal with nonlinear classification problems, rough sets introduce a human-focused way to deal with uncertainty in learning problems.
Qinghua Hu +3 more
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On commutators of Marcinkiewicz integrals with rough kernel
Journal of Mathematical Analysis and Applications, 2002 Let \(h(t)\) be a bounded function on \(\mathbb{R}^+\) and \(b\) a function in the \(\text{BMO}(\mathbb{R}^n)\) space. In this paper, the authors consider the higher-order commutator \(\mu^m_{\Omega,b}(f)\) of the Marcinkiewicz integral defined by \[ \mu^m_{\Omega,b}(f)(x)= \Biggl\{\int^\infty_0|F^m_{t, b}(x)|^2 t^{-3} dt\Biggr\}^{1/2}, \] where \[ F ...
Shanzhen Lu
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2009
Kernel machines and rough sets are two classes of popular learning techniques. Kernel machines enhance traditional linear learning algorithms to deal with nonlinear domains by a nonlinear mapping, while rough sets introduce a human-like manner to deal with uncertainty in learning.
Qinghua Hu +3 more
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Kernel machines and rough sets are two classes of popular learning techniques. Kernel machines enhance traditional linear learning algorithms to deal with nonlinear domains by a nonlinear mapping, while rough sets introduce a human-like manner to deal with uncertainty in learning.
Qinghua Hu +3 more
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Applications of Boolean Kernels in Rough Sets
2014Rough 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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The Kernel Rough K-Means Algorithm
Recent Advances in Computer Science and Communications, 2020Background: 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 +4 more
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Maximal operators with rough kernels on product domains [PDF]
Journal of Mathematical Analysis and Applications, 2005 In this paper, we study the Lp boundedness of certain maximal operators on product domains with rough kernels in L(logL).
Ahmad Al-Salman
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Parabolic Littlewood-Paley g-function with rough kernel
Acta Mathematica Sinica, English Series, 2008zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yong Ding
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A rough fuzzy kernel clustering algorithm
2015 IEEE International Conference on Communication Problem-Solving (ICCP), 2015Traditional 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 +3 more
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Rough Kernel Clustering Algorithm with Adaptive Parameters
2011Through 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 +4 more
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