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Non-Linear Extension of Generalized Hyperplane ...
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To extend GHA into a non-linear regression for larger convergence range, we derive theoretical equations and verify this extension's effectiveness and ...
Non-Linear Extension of Generalized Hyperplane Approximation
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A non-linear extension of generalized hyperplane approximation (GHA) method is introduced in this letter. Although GHA achieved a high-confidence result in ...
Non-Linear Extension of Generalized Hyperplane ...
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由 HC CHOI 著作2016 — SUMMARY. A non-linear extension of generalized hyperplane approx- imation (GHA) method is introduced in this letter. Although GHA achieved.
Non-Linear Extension of Generalized Hyperplane Approximation
The Institute of Electronics, Information and Communication Engineers
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The Institute of Electronics, Information and Communication Engineers
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Summary: A non-linear extension of generalized hyperplane approximation (GHA) method is introduced in this letter. ... To extend GHA into a non-linear regression ...
Robust segment-based object tracking using generalized ...
ScienceDirect.com
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由 HC Choi 著作2012被引用 10 次 — ▻ Generalized hyperplane approximation makes mapping from non-linear feature to motion parameter possible. ▻ Robustness against partial occlusion is ...
Robust segment-based object tracking using generalized ...
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About 10 years later, generalized hyperplane approximation (GHA) [5] was proposed to extend HA into the nonlinear feature space beyond pixel intensity. Non- ...
Nonlinear dimensionality reduction
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Nonlinear dimensionality reduction, also known as manifold learning, is any of various related techniques that aim to project high-dimensional data
Robust segment-based object tracking using generalized ...
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This method finds facial feature points very fast and accurately, since it utilizes statistical reasoning from all the training data without need to extract ...
Essential Math for Machine Learning: Hyperplanes
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2024年1月31日 — This blog post aims to demystify hyperplanes, elucidating their fundamental properties, how they're defined, and how we can manipulate them for practical ...
Adaptive quadratures for nonlinear approximation of low- ...
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由 A Magueresse 著作2024被引用 6 次 — We propose adaptive quadratures for the accurate integration of neural networks and apply them to loss functions appearing in low-dimensional PDE ...