Exploring the math in Support Vector Machines

#Machine #Learning #SVM #Kernel #Machines #Convex #Optimization #Hilbert #Space #IEEEDay

Free Registration: https://www.eventbrite.com/e/exploring-the-math-in-support-vector-machines-tickets-425130124647


“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.

  Date and Time




  • Date: 04 Oct 2022
  • Time: 06:15 PM to 08:00 PM
  • All times are (GMT-08:00) US/Pacific
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  • Starts 22 September 2022 11:02 AM
  • Ends 04 October 2022 11:03 AM
  • All times are (GMT-08:00) US/Pacific
  • No Admission Charge


Dr Pendyala


Exploring the math in Support Vector Machines

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.


Dr. Pendyala is with the Applied Data Science department at SJSU

Vishnu S. Pendyala