Results 21 to 30 of about 1,581,096 (371)
On Hardy kernels as reproducing kernels
Canadian Mathematical Bulletin, 2022 AbstractHardy kernels are a useful tool to define integral operators on Hilbertian spaces like $L^2(\mathbb R^+)$ or $H^2(\mathbb C^+)$ . These kernels entail an algebraic $L^1$ -structure which is used in this work to study the range spaces of those operators as reproducing kernel Hilbert spaces. We obtain their reproducing kernels, which in the $
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Hierarchical Kernel and Sub-kernels [PDF]
American Review of Mathematics and Statistics, 2017 This paper shows the theoretical development of hierarchy by kernels and an algorithm used to obtain an interesting class or partition from a hierarchy. Also shown is the theorem about the Kernels Optimal Criterion and how it is expressed as a function of the masses of the points of the vector space and product scale points, the inertia of the cloud ...
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Large Kernel Matters — Improve Semantic Segmentation by Global Convolutional Network [PDF]
Computer Vision and Pattern Recognition, 2017 One of recent trends [31, 32, 14] in network architecture design is stacking small filters (e.g., 1x1 or 3x3) in the entire network because the stacked small filters is more efficient than a large kernel, given the same computational complexity. However,
Chao Peng +4 more
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A Kernel Method for the Two-Sample-Problem [PDF]
Neural Information Processing Systems, 2006 We propose two statistical tests to determine if two samples are from different distributions. Our test statistic is in both cases the distance between the means of the two samples mapped into a reproducing kernel Hilbert space (RKHS).
A. Gretton +4 more
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, 2016 We introduce the Mondrian kernel, a fast random feature approximation to the Laplace kernel. It is suitable for both batch and online learning, and admits a fast kernel-width-selection procedure as the random features can be re-used efficiently for all kernel widths.
Balog, M +4 more
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IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010 Object matching is a fundamental operation in data analysis. It typically requires the definition of a similarity measure between the classes of objects to be matched. Instead, we develop an approach which is able to perform matching by requiring a similarity measure only within each of the classes. This is achieved by maximizing the dependency between
Novi Quadrianto +3 more
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Blind Super-Resolution With Iterative Kernel Correction [PDF]
Computer Vision and Pattern Recognition, 2019 Deep learning based methods have dominated super-resolution (SR) field due to their remarkable performance in terms of effectiveness and efficiency. Most of these methods assume that the blur kernel during downsampling is predefined/known (e.g., bicubic).
Jinjin Gu, Hannan Lu, W. Zuo, Chao Dong
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Kernels in digraphs that are not kernel perfect
Discrete Mathematics, 2012 AbstractAn equivalent of kernel existence is formulated using semikernels. It facilitates inductive arguments, which allow us to establish several sufficient conditions for the existence of kernels in finite digraphs. The conditions identify classes of digraphs that have kernels without necessarily being kernel perfect.
Michal Walicki, Sjur K. Dyrkolbotn
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Crop Journal, 2016 Understanding the effects of shading after pollination on kernel filling and physicochemical properties of waxy maize could improve kernel quality. Experiments were conducted to investigate the effects of shading (30% and 50% light deprivation, taken ...
Huan Yang +4 more
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Study of the low intensity spectral bands within the infrared spectra of kernels of high-yielding maize hybrids [PDF]
Zbornik Matice Srpske za Prirodne Nauke, 2019 The nature, role, and significance of low intensity spectral bands contained within the infrared spectra of kernels of high-yielding maize hybrids: ZP 341, ZP 434, and ZP 505 were observed in this study.
Radenović Čedomir N. +7 more
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