Results 11 to 20 of about 214,138 (256)
, 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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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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Speeding up quantum dissipative dynamics of open systems with kernel methods
New Journal of Physics, 2021 The future forecasting ability of machine learning (ML) makes ML a promising tool for predicting long-time quantum dissipative dynamics of open systems. In this article, we employ nonparametric ML algorithm (kernel ridge regression as a representative of
Arif Ullah, Pavlo O. Dral
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Physics-aware nonparametric regression models for Earth data analysis
Environmental Research Letters, 2022 Process understanding and modeling is at the core of scientific reasoning. Principled parametric and mechanistic modeling dominated science and engineering until the recent emergence of machine learning (ML).
Jordi Cortés-Andrés +9 more
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IEEE Access, 2023 One of the powerful techniques in data modeling is accounting for features that are available at the training stage, but are not available when the trained model is used to classify or predict test data — Learning Using Privileged Information ...
Andrey Lange +2 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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Journal of Telecommunications and Information Technology, 2022 In recent years, kernel methods have provided an important alternative solution, as they offer a simple way of expanding linear algorithms to cover the non-linear mode as well.
Rachid Fateh, Anouar Darif, Said Safi
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