Results 21 to 30 of about 88,361 (282)

Adaptive Sparse Grid Classification Using Grid Environments [PDF]

, 2007
Common techniques tackling the task of classification in data mining employ ansatz functions associated to training data points to fit the data as well as possible. Instead, the feature space can be discretized and ansatz functions centered on grid points can be used.
Dirk Pflüger, Ioan Lucian Muntean, Hans-Joachim Bungartz   +2 more
openaire   +1 more source

Multiple sparse-grid Gauss–Hermite filtering

Applied Mathematical Modelling, 2016
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Radhakrishnan, Rahul, Singh, Abhinoy Kumar, Bhaumik, Shovan, Tomar, Nutan Kumar   +3 more
openaire   +2 more sources

Multilevel Quasi-Interpolation on Chebyshev Sparse Grids

Computation
This paper investigates the potential of utilising multilevel quasi-interpolation techniques on Chebyshev sparse grids for complex numerical computations.
Faisal Alsharif
doaj   +1 more source

Fast deconvolved beamforming for arbitrary arrays based on off-grid sparse Bayesian learning [PDF]

JASA Express Letters
The deconvolved beamforming (dCv) improves spatial resolution without expanding the array aperture but fails for the shift-variant beam pattern and the real targets, which are not located on the sampling grids.
Jianli Huang, Yu Wang, Zaixiao Gong, Haiqiang Niu, Jun Wang, Haibin Wang   +5 more
doaj   +1 more source

Large-scale crop mapping from multi-source optical satellite imageries using machine learning with discrete grids

International Journal of Applied Earth Observations and Geoinformation, 2021
The spatial distribution of crops is an important agricultural parameter, which is used to derive important information about crop productivity and food security.
Shuai Yan, Xiaochuang Yao, Dehai Zhu, Diyou Liu, Lin Zhang, Guojiang Yu, Bingbo Gao, Jianyu Yang, Wenju Yun   +8 more
doaj   +1 more source

Adaptive sparse grids

ANZIAM Journal, 2003
Summary: Sparse grids, as studied by Zenger and Griebel in the last 10 years have been very successful in the solution of partial differential equations, integral equations and classification problems. Adaptive sparse grid functions are elements of a function space lattice. Such lattices allow the generalisation of sparse grid techniques to the fitting
openaire   +3 more sources

Efficient cosmological parameter sampling using sparse grids

, 2010
We present a novel method to significantly speed up cosmological parameter sampling. The method relies on constructing an interpolation of the CMB-log-likelihood based on sparse grids, which is used as a shortcut for the likelihood-evaluation.
Auld, Auld, Bellman, Bungartz, Bungartz, Chu, Coles, D. Pflüger, Doran, Doran, Ganapathysubramanian, Garcke, Garcke, Garcke, H.-J. Bungartz, Habib, Holtz, Hu, Jimenez, Kaplinghat, Klimke, Kosowsky, Lewis, M. Frommert, M. Reinecke, Page, Reisinger, Sandvik, Seljak, T. A. Enßlin, T. Riller, Tauber, Temlyakov, Verde, Von Petersdorff, Widmer, Zenger   +36 more
core   +1 more source

Uncertainty Quantification of geochemical and mechanical compaction in layered sedimentary basins [PDF]

, 2017
In this work we propose an Uncertainty Quantification methodology for sedimentary basins evolution under mechanical and geochemical compaction processes, which we model as a coupled, time-dependent, non-linear, monodimensional (depth-only) system of PDEs
Colombo, Ivo, Nobile, Fabio, Porta, Giovanni, Scotti, Anna, Tamellini, Lorenzo   +4 more
core   +2 more sources

An Improved Compressive Sensing and Received Signal Strength-Based Target Localization Algorithm with Unknown Target Population for Wireless Local Area Networks

Sensors, 2017
In this paper a two-phase compressive sensing (CS) and received signal strength (RSS)-based target localization approach is proposed to improve position accuracy by dealing with the unknown target population and the effect of grid dimensions on position ...
Jun Yan, Kegen Yu, Ruizhi Chen, Liang Chen   +3 more
doaj   +1 more source

Kernel Interpolation with Sparse Grids

, 2023
Structured kernel interpolation (SKI) accelerates Gaussian process (GP) inference by interpolating the kernel covariance function using a dense grid of inducing points, whose corresponding kernel matrix is highly structured and thus amenable to fast linear algebra.
Yadav, Mohit, Sheldon, Daniel, Musco, Cameron   +2 more
openaire   +2 more sources