Applying the possibilistic C-means algorithm in kernel-induced spaces [PDF]
, 2010 In this paper, we study a kernel extension of the classic possibilistic c-means. In the proposed extension, we implicitly map input patterns into a possibly high-dimensional space by means of positive semidefinite kernels. In this new space, we model the
Masulli, F. +5 more
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Kernel-based clustering via Isolation Distributional Kernel
, 2023 Clustering has become one of the widely used automatic data-labeling techniques applied in a variety of disciplines. Kernel-based clustering is a technique designed to identify non-linearly separable clusters with irregular shapes.
Ye Zhu (13101348), KM Ting (13907250)
core
On Symmetrical Sonin Kernels in Terms of Hypergeometric-Type Functions
MathematicsIn this paper, a new class of kernels of integral transforms of the Laplace convolution type that we named symmetrical Sonin kernels is introduced and investigated. For a symmetrical Sonin kernel given in terms of elementary or special functions, its associated kernel has the same form with possibly different parameter values. In the paper, several new
openaire +2 more sources
Nonlinear Knowledge in Kernel Approximation [PDF]
, 2006 Prior knowledge over arbitrary general sets is incorporated into nonlinear kernel approximation problems in the form of linear constraints in a linear program.
Mangasarian, Olvi, Wild, Edward
core
Time fractional Yang-Abdel-Cattani derivative in generalized MHD Casson fluid flow with heat source and chemical reaction. [PDF]
Sci Rep, 2023 Sehra, Sadia H, Haq SU, Khan I.
europepmc +1 more source
kernlab - An S4 package for kernel methods in R [PDF]
, 2004 kernlab is an extensible package for kernel-based machine learning methods in R. It takes advantage of R's new S4 object model and provides a framework for creating and using kernel-based algorithms. The package contains dot product primitives (kernels),
Zeileis, Achim +10 more
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Sparse Kernel Modelling: A Unified Approach
, 2007 A unified approach is proposed for sparse kernel data modelling that includes regression and classification as well as probability density function estimation. The orthogonal-least-squares forward selection method based on the leave-one-out test criteria
Hong, X., Harris, C.J., Chen, S.
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High-Degree Collisional Moments of Inelastic Maxwell Mixtures-Application to the Homogeneous Cooling and Uniform Shear Flow States. [PDF]
Entropy (Basel), 2023 Sánchez Romero C, Garzó V.
europepmc +1 more source
General Non-Markovian Quantum Dynamics. [PDF]
Entropy (Basel), 2021 Tarasov VE.
europepmc +1 more source
Improved Quantum-Classical Treatment of N2-N2 Inelastic Collisions: Effect of the Potentials and Complete Rate Coefficient Data Sets. [PDF]
J Chem Theory Comput, 2023 Hong Q +5 more
europepmc +1 more source