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Thesis-Jay-Schaffer.pdf (373.53 KB)

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The Kernel Method: Reproducing Kernel Hilbert Spaces in Application

Schaffer, Paul J
http://rave.ohiolink.edu/etdc/view?acc_num=ouhonors1682691587736024

Abstract Details

2023, Bachelor of Science (BS), Ohio University, Mathematics.
We discuss the theoretical background of the kernel method, a machine learning technique derived from the theory of reproducing kernel Hilbert spaces. We then discuss the kernel method and how it can tie in to other modern machine learning techniques such as artificial neural networks.
Alexei Davydov (Advisor)
Adam Fuller (Advisor)
82 p.
Mathematics
Reproducing kernel Hilbert spaces, Kernel Method, Rectifier linear unit activation function, Neural networks

Recommended Citations

Citations

  • Schaffer, P. J. (2023). The Kernel Method: Reproducing Kernel Hilbert Spaces in Application [Undergraduate thesis, Ohio University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ouhonors1682691587736024

    APA Style (7th edition)

  • Schaffer, Paul. The Kernel Method: Reproducing Kernel Hilbert Spaces in Application. 2023. Ohio University, Undergraduate thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ouhonors1682691587736024.

    MLA Style (8th edition)

  • Schaffer, Paul. "The Kernel Method: Reproducing Kernel Hilbert Spaces in Application." Undergraduate thesis, Ohio University, 2023. http://rave.ohiolink.edu/etdc/view?acc_num=ouhonors1682691587736024

    Chicago Manual of Style (17th edition)

ouhonors1682691587736024
154
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This open access ETD is published by Ohio University Honors Tutorial College and OhioLINK.