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DTSTART:20220313T030000
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DTSTART:20221106T010000
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DTSTAMP:20221005T174624Z
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DTSTART;TZID=US/Pacific:20221004T181500
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DESCRIPTION:Free Registration: https://www.eventbrite.com/e/exploring-the-m
 ath-in-support-vector-machines-tickets-425130124647\n\nSynopsis:\n\n“SVM
 s are a rare example of a methodology where geometric intuition\, elegant 
 mathematics\, theoretical guarantees\, and practical algorithms meet” 
 – Bennet and Campbell\n\nSupport Vector Machines (SVMs) are used for sup
 ervised machine learning and have been successful in many applications inc
 luding those like image classification that favor deep learning. SVM owes 
 its power to the intriguing math involved in its fabrication. This talk wi
 ll introduce SVM and cover some of that math. Topics covered will include 
 constrained and unconstrained optimization\, convexity\, the general notio
 n of a function space\, minmax equilibrium\, duality\, Cover theorem\, Ker
 nels\, and Mercer theorem.\n\nSpeaker(s): Dr Pendyala\, Vishnu S. Pendyala
 \n\nVirtual: https://events.vtools.ieee.org/m/324950
LOCATION:Virtual: https://events.vtools.ieee.org/m/324950
ORGANIZER:pendyala@ieee.org
SEQUENCE:1
SUMMARY:Exploring the math in Support Vector Machines
URL;VALUE=URI:https://events.vtools.ieee.org/m/324950
X-ALT-DESC:Description: &lt;br /&gt;&lt;p&gt;Free Registration:&amp;nbsp\;&lt;a href=&quot;https://
 www.eventbrite.com/e/exploring-the-math-in-support-vector-machines-tickets
 -425130124647&quot;&gt;https://www.eventbrite.com/e/exploring-the-math-in-support-
 vector-machines-tickets-425130124647&lt;/a&gt;&lt;/p&gt;\n&lt;p&gt;&lt;em&gt;&lt;strong&gt;Synopsis:&lt;/st
 rong&gt;&lt;/em&gt;&lt;/p&gt;\n&lt;p&gt;&lt;em&gt;&amp;ldquo\;SVMs are a rare example of a methodology wh
 ere geometric intuition\, elegant mathematics\, theoretical guarantees\, a
 nd practical algorithms meet&amp;rdquo\; &amp;ndash\; Bennet and Campbell&lt;/em&gt;&lt;/p&gt;
 \n&lt;p&gt;Support Vector Machines (SVMs) are used for supervised machine learni
 ng and have been successful in many applications including those like imag
 e classification that favor deep learning. SVM owes its power to the intri
 guing math involved in its fabrication. This talk will introduce SVM and c
 over some of that math. Topics covered will include constrained and uncons
 trained optimization\, convexity\, the general notion of a function space\
 , minmax equilibrium\, duality\, Cover theorem\, Kernels\, and Mercer theo
 rem.&lt;/p&gt;
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