Results 21 to 30 of about 248,031 (304)
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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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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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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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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A Novel Boolean Kernels Family for Categorical Data
Entropy, 2018 Kernel based classifiers, such as SVM, are considered state-of-the-art algorithms and are widely used on many classification tasks. However, this kind of methods are hardly interpretable and for this reason they are often considered as black-box models ...
Mirko Polato +2 more
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Kernel Methods for Surrogate Modeling
CoRR, 2019 This chapter deals with kernel methods as a special class of techniques for surrogate modeling. Kernel methods have proven to be efficient in machine learning, pattern recognition and signal analysis due to their flexibility, excellent experimental performance and elegant functional analytic background.
Santin G., Haasdonk B.
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Bandwidth Selection Problem in Nonparametric Functional Regression [PDF]
Statistika: Statistics and Economy Journal, 2017 The focus of this paper is the nonparametric regression where the predictor is a functional random variable, and the response is a scalar. Functional kernel regression belongs to popular nonparametric methods used for this purpose. The two key problems
Daniela Kuruczová, Jan Koláček
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Estimating Road Segments Using Kernelized Averaging of GPS Trajectories
Applied Sciences, 2019 A method called iTEKA, which stands for iterative time elastic kernel averaging, was successfully used for averaging time series. In this paper, we adapt it to GPS trajectories. The key contribution is a denoising procedure that includes an over-sampling
Pierre-François Marteau
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Approximate inference of the bandwidth in multivariate kernel density estimation [PDF]
, 2011 Kernel density estimation is a popular and widely used non-parametric method for data-driven density estimation. Its appeal lies in its simplicity and ease of implementation, as well as its strong asymptotic results regarding its convergence to the true ...
Sanguinetti, G. +3 more
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