publication . Preprint . 2019

Probabilistic Kernel Support Vector Machines

Chen, Yongxin; Georgiou, Tryphon T.; Tannenbaum, Allen R.;
Open Access English
  • Published: 14 Apr 2019
Abstract
Comment: 6 pages, 6 figures
Subjects
free text keywords: Computer Science - Machine Learning, Electrical Engineering and Systems Science - Systems and Control, Statistics - Machine Learning, 62G05, 93A30
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NIH| MOUSE GENETICS
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  • Funder: National Institutes of Health (NIH)
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NSF| Collaborative Research: Dynamics of Densities: Modeling, Control and Estimation
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  • Funder: National Science Foundation (NSF)
  • Project Code: 1807664
  • Funding stream: Directorate for Engineering | Division of Electrical, Communications & Cyber Systems
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NSF| EAGER: Real-Time: Search for dynamical dependencies and natural time-scales of physical processes
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  • Funder: National Science Foundation (NSF)
  • Project Code: 1839441
  • Funding stream: Directorate for Engineering | Division of Electrical, Communications & Cyber Systems
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NIH| Glymphatic function in a transgenic rat model of Alzheimer's disease
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  • Funder: National Institutes of Health (NIH)
  • Project Code: 5R01AG048769-04
  • Funding stream: NATIONAL INSTITUTE ON AGING
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NIH| Dose-distribution radiomics to predict morbidity risk in radiotherapy
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  • Funder: National Institutes of Health (NIH)
  • Project Code: 5R01CA198121-02
  • Funding stream: NATIONAL CANCER INSTITUTE
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[3] N. Aronszajn, “Theory of reproducing kernels,” Transactions of the American mathematical society, vol. 68, no. 3, pp. 337-404, 1950. [OpenAIRE]

[4] D. Alpay, “An advanced complex analysis problem book,” Topological vector spaces, functional analysis, and Hilbert spaces of analytic functions. Birka¨user Basel, 2015.

[5] B. Scholkopf and A. J. Smola, Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT press, 2001.

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