Results 21 to 30 of about 48,550 (262)
Algorithms for optimal outlier removal
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Rossen Atanassov +7 more
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An integrated approach for identifying wrongly labelled samples when performing classification in microarray data. [PDF]
Using hybrid approach for gene selection and classification is common as results obtained are generally better than performing the two tasks independently.
Yuk Yee Leung +2 more
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An Adversarial Optimization Approach to Efficient Outlier Removal [PDF]
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Jin Yu 0001 +3 more
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Feature-based remote sensing image registration methods have achieved great accomplishments. However, they have faced some limitations of applicability, automation, accuracy, efficiency, and robustness for large high-resolution remote sensing image ...
Junyan Lu +5 more
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Long-range surveillance and early warning of space targets are significant factors in space security. Under remote observation conditions, the energy performance of the target is weak and subject to environmental and imaging process contamination.
Chenyu Xu +5 more
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A Method for Removing Outlier Noise from ArrayInSAR Point Clouds based on Hybrid Filtering [PDF]
Array Interferometric Synthetic Aperture Radar (ArrayInSAR) point cloud is a point cloud obtained by three-dimensional imaging of two-dimensional Synthetic Aperture Radar (SAR) images using array interferometric synthetic aperture radar technology, which
F. Ji +6 more
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Data harmonization is a key step widely used in multisite neuroimaging studies to remove inter-site heterogeneity of data distribution. However, data harmonization may even introduce additional inter-site differences in neuroimaging data if outliers are ...
Qichao Han +6 more
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Outlier Removal in 2D Leap Frog Algorithm [PDF]
In this paper a 2D Leap Frog Algorithm is applied to solve the so-called noisy Photometric Stereo problem. In 3-source Photometric Stereo (noiseless or noisy) an ideal unknown Lambertian surface is illuminated from distant light-source directions (their directions are assumed to be linearly independent).
Ryszard Kozera, Tchórzewski, Jacek
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A Unified Sparse Optimization Framework to Learn Parsimonious Physics-Informed Models From Data
Machine learning (ML) is redefining what is possible in data-intensive fields of science and engineering. However, applying ML to problems in the physical sciences comes with a unique set of challenges: scientists want physically interpretable models ...
Kathleen Champion +4 more
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Robust Time-of-Arrival Location Estimation Algorithms for Wildlife Tracking
Time-of-arrival transmitter localization systems, which use measurements from an array of sensors to estimate the location of a radio or acoustic emitter, are now widely used for tracking wildlife.
Eitam Arnon +5 more
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