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KERNEL LOGISTIC REGRESSION-LINEAR FOR LEUKEMIA CLASSIFICATION USING HIGH DIMENSIONAL DATA [PDF]
JUTI: Jurnal Ilmiah Teknologi Informasi, 2009 Kernel Logistic Regression (KLR) is one of the statistical models that has been proposed for classification in the machine learning and data mining communities, and also one of the effective methodologies in the kernel–machine techniques.
S P Rahayu +3 more
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Henderson's method approach to Kernel prediction in partially linear mixed models
Cumhuriyet Science Journal, 2020 In this article, we propose Kernel prediction in partially linear mixed models by using Henderson's method approach. We derive the Kernel estimator and the Kernel predictor via the mixed model equations (MMEs) of Henderson's that they give the best ...
Seçil Yalaz, Özge Kuran
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Performance Portability Study of Linear Algebra Kernels in OpenCL [PDF]
, 2014 The performance portability of OpenCL kernel implementations for common memory bandwidth limited linear algebra operations across different hardware generations of the same vendor as well as across vendors is studied.
Grasser, Tibor +5 more
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A Linear Kernel for Planar Total Dominating Set [PDF]
Discrete Mathematics & Theoretical Computer Science, 2018 A total dominating set of a graph $G=(V,E)$ is a subset $D \subseteq V$ such that every vertex in $V$ is adjacent to some vertex in $D$. Finding a total dominating set of minimum size is NP-hard on planar graphs and W[2]-complete on general graphs when ...
Valentin Garnero, Ignasi Sau
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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
core +6 more sources
Kernel Density Estimated Linear Regression
Proceedings of the International Florida Artificial Intelligence Research Society ConferenceRegression analysis is a cornerstone of predictive modeling, with linear regression and kernel regression standing as two of its most prominent paradigms.
Roshan Kalpavruksha +3 more
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Block-encoding dense and full-rank kernels using hierarchical matrices: applications in quantum numerical linear algebra [PDF]
Quantum, 2022 Many quantum algorithms for numerical linear algebra assume black-box access to a block-encoding of the matrix of interest, which is a strong assumption when the matrix is not sparse.
Quynh T. Nguyen +2 more
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Frontiers in Pharmacology, 2023 The target of the study is to predict the inhibitory effect of amide derivatives on xanthine oxidase (XO) by building several models, which are based on the theory of the quantitative structure–activity relationship (QSAR).
Xiaoda Yang +3 more
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Study on radar echo image quality control based on improved convolution technology
暴雨灾害, 2022 Based on the principle of convolution calculation,this study improves the conventional convolution method and constructs the isolated point convolution kernel,linear convolution kernel,and weak echo convolution kernel. Based on this improved conventional
Daoyang NIE, An XIAO, Houjie XIA
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Determining kernels in linear viscoelasticity [PDF]
Journal of Computational Physics, 2022 In this work, we investigate the inverse problem of determining the kernel functions that best describe the mechanical behavior of a complex medium modeled by a general nonlocal viscoelastic wave equation. To this end, we minimize a tracking-type data misfit function under this PDE constraint.
Barbara Kaltenbacher +4 more
openaire +5 more sources