Results 251 to 260 of about 46,215 (298)

Data inherent assessment of electrical equipment by failure pattern and self-evaluation decision-making algorithm. [PDF]

Sci Rep
Aranizadeh A, Vahidi B, Khorsandi A.
europepmc   +1 more source

Support Vector Regression for Outliers Removal

International Journal of Scientific Engineering and Research, 2014
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Real-time outlier detection in digital PCR data for wastewater-based pathogen surveillance

McLeod RE, Julian TR, Stadler T, Lison A.   +3 more
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Guaranteed Outlier Removal for Rotation Search

2015 IEEE International Conference on Computer Vision (ICCV), 2015
Rotation search has become a core routine for solving many computer vision problems. The aim is to rotationally align two input point sets with correspondences. Recently, there is significant interest in developing globally optimal rotation search algorithms. A notable weakness of global algorithms, however, is their relatively high computational cost,
Álvaro Parra Bustos, Tat-Jun Chin
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Automated Outlier Removal for Mobile Microbenchmarking Datasets

2015 10th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), 2015
Microbenchmarking is a useful tool for fine-grained performance analysis, and represents a potentially valuable tool in the development of mobile applications and systems. However, the fine-grained measurements of microbenchmarking are inherently susceptible to noise from the underlying operating system and hardware.
Adam Rehn, Jason Holdsworth, Ickjai Lee
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k -means clustering with outlier removal

Pattern Recognition Letters, 2017
We study the problem of data clustering with outlier detection.We propose a k-means-type algorithm by incorporating an additional cluster into the objective function.The algorithm is able to provide data clustering and outlier detection simultaneously.Outliers are not used in the cluster center calculation.Experiments on synthetic and real data show ...
Guojun Gan, Michael K Ng
exaly   +2 more sources

Guaranteed Outlier Removal with Mixed Integer Linear Programs

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016
The maximum consensus problem is fundamentally important to robust geometric fitting in computer vision. Solving the problem exactly is computationally demanding, and the effort required increases rapidly with the problem size. Although randomized algorithms are much more efficient, the optimality of the solution is not guaranteed.
Tat-Jun Chin, Yang Heng Kee, Anders P. Eriksson, Frank Neumann 0001   +3 more
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Quasi-interpolation and outliers removal

Numerical Algorithms, 2017
The authors develop a method for removing outliers using quasi-interpolation. The authors use quasi-interpolation and the approximation error of a function to create a boundary beyond which a data point is adjudicated as an outlier and removed from the dataset.
Anat Amir, David Levin
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Gamma Mixture Models for Outlier Removal

2018 25th IEEE International Conference on Image Processing (ICIP), 2018
In this paper, we introduce a probabilistic outlier model which is seamlessly integrated into machine learning frameworks (e.g., boosting and deep neural network) to accurately identify outliers in training samples. With two Gamma mixtures, the proposed model can estimate the distribution of inlier and outlier samples respectively and generates their ...
Xin Wu, Ling Cai 0003, Rongrong Ji
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Optimal outlier removal in high-dimensional

Proceedings of the thirty-third annual ACM symposium on Theory of computing, 2001
We study the problem of finding an outlier-free subset of a set of points (or a probability distribution) in n-dimensional Euclidean space. A point x is defined to be a β-outlier if there exists some direction w in which its squared distance from the mean along w is greater than β times the average squared distance from the mean along w [1].
John Dunagan, Santosh S. Vempala
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