Results 11 to 20 of about 168,042 (312)
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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Widely Linear Kernels for Complex-valued Kernel Activation Functions [PDF]
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019 Accepted at ICASSP ...
Scardapane, S +3 more
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Efficient linearization of tree kernel functions [PDF]
Proceedings of the Thirteenth Conference on Computational Natural Language Learning - CoNLL '09, 2009 The combination of Support Vector Machines with very high dimensional kernels, such as string or tree kernels, suffers from two major drawbacks: first, the implicit representation of feature spaces does not allow us to understand which features actually triggered the generalization; second, the resulting computational burden may in some cases render ...
Pighin, Daniele, Moschitti, Alessandro
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A Comparative Analysis of Kernel Subspace Target Detectors for Hyperspectral Imagery
EURASIP Journal on Advances in Signal Processing, 2007 Several linear and nonlinear detection algorithms that are based on spectral matched (subspace) filters are compared. Nonlinear (kernel) versions of these spectral matched detectors are also given and their performance is compared with linear versions ...
Nasser M. Nasrabadi, Heesung Kwon
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Fast Algorithms for Linear and Kernel SVM+ [PDF]
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 The SVM+ approach has shown excellent performance in visual recognition tasks for exploiting privileged information in the training data. In this paper, we propose two efficient algorithms for solving the linear and kernel SVM+, respectively. For linear SVM+, we absorb the bias term into the weight vector, and formulate a new optimization problem with ...
Wen Li +4 more
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Universal Local Linear Kernel Estimators in Nonparametric Regression
Mathematics, 2022 New local linear estimators are proposed for a wide class of nonparametric regression models. The estimators are uniformly consistent regardless of satisfying traditional conditions of dependence of design elements.
Yuliana Linke +5 more
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A Linear Kernel for Planar Vector Domination
CoRR, 2023 Given a graph $G$, an integer $k\geq 0$, and a non-negative integral function $f:V(G) \rightarrow \mathcal{N}$, the Vector Domination problem asks whether a set $S$ of vertices, of cardinality $k$ or less, exists in $G$ so that every vertex $v \in V(G)\setminus S$ has at least $f(v)$ neighbors in $S$. The problem generalizes several domination problems
Mahabba El Sahili, Faisal N. Abu-Khzam
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Revista Tecnológica, 2017 El objetivo del trabajo fue evaluar la eficiencia del clasificador Máquina de Soporte Vectorial (SVM) con el método de separación linear, cuando se utiliza el algoritmo de extracción de características Histograma Orientado a Gradientes (HOG) y cuando no ...
Jessica Johanna Morales Carrillo
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Journal of Numerical Analysis and Approximation Theory, 2023 Our aim in this work is to extend the primal-dual interior point method based on a kernel function for linear fractional problem. We apply the techniques of kernel function-based interior point methods to solve a standard linear fractional program.
Mousaab Bouafia, Adnan Yassine
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Weighted p-norm distance t kernel SVM classification algorithm based on improved polarization
Scientific Reports, 2022 The kernel function in SVM enables linear segmentation in a feature space for a large number of linear inseparable data. The kernel function that is selected directly affects the classification performance of SVM.
Wenbo Liu, Shengnan Liang, Xiwen Qin
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