Results 11 to 20 of about 12,640,042 (291)
Stability and covergence properties of Bergman Kernel methods for numerical conformal mapping [PDF]
, 1984 In this paper we study the stability and convergence properties of Bergman kernel methods, for the numerical conform al mapping of simply and doubly- connected domains.
Warby, M K, Papamichael, N
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, 2023 This chapter introduces a powerful class of machine learning approaches called kernel methods, which present an alternative to arguably more widely known neural network approaches. Kernel methods can learn even highly nonlinear problems by making an implicit transformation from a low-dimensional input space into a higher-dimensional feature space. This
Pinheiro Jr, Max, Dral, Pavlo
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The kernel polynomial method [PDF]
Reviews of Modern Physics, 2006 Efficient and stable algorithms for the calculation of spectral quantities and correlation functions are some of the key tools in computational condensed matter physics. In this article we review basic properties and recent developments of Chebyshev expansion based algorithms and the Kernel Polynomial Method.
Weiße, Alexander +3 more
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Bernoulli, 2022 This paper proposes and studies a numerical method for approximation of posterior expectations based on interpolation with a Stein reproducing kernel. Finite-sample-size bounds on the approximation error are established for posterior distributions supported on a compact Riemannian manifold, and we relate these to a kernel Stein discrepancy (KSD ...
Barp A +3 more
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A Kernel Method for Classification [PDF]
, 2004 Kernel Maximum Likelihood Hebbian Learning Scale Invariant Maps is a novel technique developed to facilitate the clustering of complex data effectively and efficiently and that is characterised for converging remarkably quickly. The combination of Maximum Likelihood Hebbian Learning Scale Invariant Map and the Kernel Space provides a very smooth scale ...
MacDonald, Donald +4 more
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A survey of kernel and spectral methods for clustering [PDF]
, 2007 Clustering algorithms are a useful tool to explore data structures and have been employed in many disciplines. The focus of this paper is the partitioning clustering problem with a special interest in two recent approaches: kernel and spectral methods ...
Masulli, F. +11 more
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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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Information Theory With Kernel Methods
IEEE Transactions on Information Theory, 2023 We consider the analysis of probability distributions through their associated covariance operators from reproducing kernel Hilbert spaces. We show that the von Neumann entropy and relative entropy of these operators are intimately related to the usual notions of Shannon entropy and relative entropy, and share many of their properties.
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Applied Sciences, 2021 Neural oscillations are present in the brain at different spatial and temporal scales, and they are linked to several cognitive functions. Furthermore, the information carried by their phases is fundamental for the coordination of anatomically ...
Iván De La Pava Panche +5 more
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Invariance in Kernel Methods by Haar-Integration Kernels [PDF]
, 2005 We address the problem of incorporating transformation invariance in kernels for pattern analysis with kernel methods. We introduce a new class of kernels by so called Haar-integration over transformations. This results in kernel functions, which are positive definite, have adjustable invariance, can capture simultaneously various continuous or ...
Haasdonk, Bernard +2 more
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