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Course
Machine Learning
Description
“SVMs are a rare example of a methodology where geometric intuition, elegant mathematics, theoretical guarantees, and practical algorithms meet” – Bennet and Campbell
Support Vector Machines (SVMs) are used for supervised machine learning and have been successful in many applications, including those like image classification that favor deep learning. SVM owes its power to the intriguing math involved in its fabrication. This talk will introduce SVM and cover some of that math. Topics covered will include constrained and unconstrained optimization, convexity, the general notion of a function space, minmax equilibrium, duality, the Cover theorem, Kernels, and the Mercer theorem.
More Details: https://events.vtools.ieee.org/m/324950
Video Recording: https://ieeetv.ieee.org/video/exploring-the-math-in-support-vector-machines
Publication Date
Fall 9-22-2022
Document Type
Presentation
Disciplines
Computational Engineering | Computer Engineering
Creative Commons License
This work is licensed under a Creative Commons Attribution-No Derivative Works 4.0 License.
Recommended Citation
Pendyala, Vishnu, "Exploring the math in Support Vector Machines" (2022). Open Educational Resources. 11.
https://scholarworks.sjsu.edu/oer/11
