Results 131 to 140 of about 125,665 (304)
Traces of functions from H? $$(\mathbb{B}^n )$$ on certain sets of hyperplanes [PDF]
N. A. Shirokov
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Best Fitting Hyperplanes for Classification
In this paper, we propose novel methods that are more suitable than classical large-margin classifiers for open set recognition and object detection tasks. The proposed methods use the best fitting hyperplanes approach, and the main idea is to find the best fitting hyperplanes such that each hyperplane is close to the samples of one of the classes and ...
openaire +3 more sources
DQN‐Guided Subset‐Induced OCSVM Kernel Approximation for Imbalanced Anomaly Detection
Anomaly detection under limited normal data remains a fundamental challenge due to severe class imbalance and scarcity of anomalies. We propose a novel framework that reformulates support vector selection in One‐Class SVM as a sequential decision‐making problem.
Wenqian Yu, Jiaying Wu, Jinglu Hu
wiley +1 more source
Exploration of new wildlife surveying methodologies that leverage advances in sensor technology and machine learning has led to tentative research into the application of seismology techniques. This, most commonly, involves the deployment of a footfall trap – a seismic sensor and data logger customised for wildlife footfall.
Benjamin J. Blackledge +4 more
wiley +1 more source
Relative Positions Between the Hyperplane and the n-Sphere
This paper discusses are some topics Analytic Geometry, studied in basic education in the context of Euclidean space $ n $-dimensional. Presents itself for example, the concepts of hyperplane and $(n-1)$-sphere, which correspond to the high school to the
Joselito de Oliveira
doaj
An estimate for the Gauss curvature of minimal surfaces in ${\bf R}\sp m$ whose Gauss map omits a set of hyperplanes [PDF]
Robert Osserman, Min Ru
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On hyperplane sections of reduced irreducible varieties of low codimension [PDF]
Jürgen Herzog +2 more
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A Mixed Frequency BVAR for the Australian Economy*
A mixed frequency vector autoregression (MFVAR) model is proposed for nowcasting, forecasting and backcasting Australian macroeconomic indicators at monthly and quarterly frequencies. A novel augmented Minnesota prior for MFVAR models is also introduced.
Kelly Trinh, Jamie L. Cross
wiley +1 more source
Hyperplane Arrangements in the Grassmannian
The Euler characteristic of a very affine variety encodes the algebraic complexity of solving likelihood (or scattering) equations on this variety. We study this quantity for the Grassmannian with $d$ hyperplane sections removed. We provide a combinatorial formula, and explain how to compute this Euler characteristic in practice, both symbolically and ...
E. Mazzucchelli, D. Pavlov, K. Wang
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