Results 21 to 30 of about 214,138 (256)
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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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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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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Rate-Distortion Bounds for Kernel-Based Distortion Measures
Entropy, 2017 Kernel methods have been used for turning linear learning algorithms into nonlinear ones. These nonlinear algorithms measure distances between data points by the distance in the kernel-induced feature space.
Kazuho Watanabe
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Applied Sciences, 2021 Cross-frequency interactions, a form of oscillatory neural activity, are thought to play an essential role in the integration of distributed information in the brain.
Iván De La Pava Panche +4 more
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Predicting Pharmaceutical Particle Size Distributions Using Kernel Mean Embedding
Pharmaceutics, 2020 In the pharmaceutical industry, the transition to continuous manufacturing of solid dosage forms is adopted by more and more companies. For these continuous processes, high-quality process models are needed.
Daan Van Hauwermeiren +3 more
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BMC Bioinformatics, 2023 Background In this paper, we are interested in interactions between a high-dimensional -omics dataset and clinical covariates. The goal is to evaluate the relationship between a phenotype of interest and a high-dimensional omics pathway, where the effect
Charlie M. Carpenter +4 more
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, 2003 Bayesian methods allow for a simple and intuitive representation of the function spaces used by kernel methods. This chapter describes the basic principles of Gaussian Processes, their implementation and their connection to other kernel-based Bayesian estimation methods, such as the Relevance Vector Machine.
Alexander J. Smola, Bernhard Schölkopf
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eLife, 2017 Revealing the current source distribution along the neuronal membrane is a key step on the way to understanding neural computations; however, the experimental and theoretical tools to achieve sufficient spatiotemporal resolution for the estimation remain
Dorottya Cserpán +6 more
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