Meta-algorithms in Machine Learning

#Ensemble #methods; #Bagging; #Boosting; #Stacking; #Random #Forest; #Adaboost; #Gradient #boosting; #Bias; #Variance; #Overfitting; #Bootstrap #Sampling; #Aggregation; #Meta-learning; #One-shot #learning; #Few-shot #learning
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https://r6.ieee.org/scv-cs/?p=2062

The 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, Logistic Regression, and Support Vector Machines to improve the performance substantially. It is often observed that these algorithms fetch top positions in the competition leaderboards and are now commonly used in the industry 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 broad categories such as random forest, adaboost, and gradient boosting.


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  • Date: 01 Jun 2022
  • Time: 01:00 AM UTC to 03:00 AM UTC
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  • Starts 22 May 2022 07:47 PM UTC
  • Ends 31 May 2022 07:47 PM UTC
  • No Admission Charge

  Speakers

Dr Pendyala

Topic:

Meta-algorithms in Machine Learning

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.

Biography:

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

Vishnu S. Pendyala

Topic:

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