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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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Вісник Національного технічного університету "ХПÌ": Системний аналіз, управління та інформаційні технології, 2022 The problem of regression model building of equipment failure rate using datasets containing information on number of failures of recoverable systems and measurements of technological and operational factors affecting the reliability of production system
Olena Akhiiezer +3 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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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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Diagnostics, 2023 This paper uses EEG data to introduce an approach for classifying right and left-hand classes in Motor Imagery (MI) tasks. The Kernel Cross-Spectral Functional Connectivity Network (KCS-FCnet) method addresses these limitations by providing richer ...
Daniel Guillermo García-Murillo +2 more
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Kernel methods in machine learning
, 2008 We review machine learning methods employing positive definite kernels. These methods formulate learning and estimation problems in a reproducing kernel Hilbert space (RKHS) of functions defined on the data domain, expanded in terms of a kernel.
Hofmann, Thomas +2 more
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Frontiers in Genetics, 2019 Deep learning (DL) is a promising method for genomic-enabled prediction. However, the implementation of DL is difficult because many hyperparameters (number of hidden layers, number of neurons, learning rate, number of epochs, batch size, etc.) need to ...
José Crossa +8 more
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A Data-Driven Measure of Effective Connectivity Based on Renyi's α-Entropy
Frontiers in Neuroscience, 2019 Transfer entropy (TE) is a model-free effective connectivity measure based on information theory. It has been increasingly used in neuroscience because of its ability to detect unknown non-linear interactions, which makes it well suited for exploratory ...
Ivan De La Pava Panche +2 more
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A feasible k-means kernel trick under non-Euclidean feature space
International Journal of Applied Mathematics and Computer Science, 2020 This paper poses the question of whether or not the usage of the kernel trick is justified. We investigate it for the special case of its usage in the kernel k-means algorithm.
Kłopotek Robert +2 more
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