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DTSTART:20220313T030000
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DTSTART:20221106T010000
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DTSTAMP:20220601T051713Z
UID:72E5520E-9EA7-4A04-8FCE-D997C489C067
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DESCRIPTION:Please register (free) to attend:\n\nhttps://r6.ieee.org/scv-cs
 /?p=2062\n\nThe word ‘meta’ indicates something beyond\, a level up\, 
 or a higher layer. Meta-algorithms in Machine learning work on top of the 
 known classification and regression algorithms such as Decision Trees\, Lo
 gistic Regression\, and Support Vector Machines to improve the performance
  substantially. It is often observed that these algorithms fetch top posit
 ions in the competition leaderboards and are now commonly used in the indu
 stry as well. This talk will cover some of the popular techniques of Meta-
 learning and explain why they generally work well. The techniques covered 
 will include bagging\, boosting\, stacking\, and algorithms within those b
 road categories such as random forest\, adaboost\, and gradient boosting.\
 n\nSpeaker(s): Dr Pendyala\, Vishnu S. Pendyala\n\nVirtual: https://events
 .vtools.ieee.org/m/315184
LOCATION:Virtual: https://events.vtools.ieee.org/m/315184
ORGANIZER:pendyala@ieee.org
SEQUENCE:1
SUMMARY:Meta-algorithms in Machine Learning
URL;VALUE=URI:https://events.vtools.ieee.org/m/315184
X-ALT-DESC:Description: &lt;br /&gt;&lt;p&gt;Please register (free) to attend:&amp;nbsp\;&lt;/
 p&gt;\n&lt;p&gt;&lt;a href=&quot;https://r6.ieee.org/scv-cs/?p=2062&quot;&gt;https://r6.ieee.org/sc
 v-cs/?p=2062&lt;/a&gt;&lt;/p&gt;\n&lt;p&gt;The word &amp;lsquo\;meta&amp;rsquo\; indicates something
  beyond\, a level up\, or a higher layer. Meta-algorithms in Machine learn
 ing work on top of the known classification and regression algorithms such
  as Decision Trees\, Logistic Regression\, and Support Vector Machines to 
 improve the performance substantially. It is often observed that these alg
 orithms fetch top positions in the competition leaderboards and are now co
 mmonly used in the industry as well. This talk will cover some of the popu
 lar techniques of Meta-learning and explain why they generally work well. 
 The techniques covered will include bagging\, boosting\, stacking\, and al
 gorithms within those broad categories such as random forest\, adaboost\, 
 and gradient boosting.&lt;/p&gt;
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