“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, Cover theorem, Kernels, and Mercer theorem.

Speaker: Dr. Vishnu S. Pendyala is a faculty member of the Department of Applied Data Science at San Jose State University and is the Chair of the IEEE Computer Society, Silicon Valley Chapter.

Event Details

  • Vishnu Srinivas Pendyala

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Zoom information will be provided to all those who registered on Eventbrite. The event will also be live broadcasted on YouTube https://www.youtube.com/channel/UC4kaO4mrTxrCltxb0HalV3Q

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