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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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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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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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FEW-GROUP CROSS SECTIONS LIBRARY BY ACTIVE LEARNING WITH SPLINE KERNELS [PDF]
EPJ Web of Conferences, 2021 This work deals with the representation of homogenized few-groups cross sections libraries by machine learning. A Reproducing Kernel Hilbert Space (RKHS) is used for different Pool Active Learning strategies to obtain an optimal support.
Szames E. +3 more
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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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Kernel Current Source Density Method [PDF]
Neural Computation, 2011 Local field potentials (LFP), the low-frequency part of extracellular electrical recordings, are a measure of the neural activity reflecting dendritic processing of synaptic inputs to neuronal populations. To localize synaptic dynamics, it is convenient, whenever possible, to estimate the density of transmembrane current sources (CSD) generating the ...
Wójcik Daniel K +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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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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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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