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Maximum kernel likelihood estimation [PDF]
, 2008 We introduce an estimator for the population mean based on maximizing likelihoods formed by parameterizing a kernel density estimate. Due to these origins, we have dubbed the estimator the maximum kernel likelihood estimate (mkle). A speedy computational
Jaki, Thomas, West, R. Webster
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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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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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Locally linear approximation for Kernel methods : the Railway Kernel [PDF]
, 2008 In this paper we present a new kernel, the Railway Kernel, that works properly for general (nonlinear) classification problems, with the interesting property that acts locally as a linear kernel.
González, Javier, Muñoz, Alberto
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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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Random Forests and Kernel Methods [PDF]
IEEE Transactions on Information Theory, 2016 Random forests are ensemble methods which grow trees as base learners and combine their predictions by averaging. Random forests are known for their good practical performance, particularly in high dimensional set-tings. On the theoretical side, several studies highlight the potentially fruitful connection between random forests and kernel methods.
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