Meta-algorithms in Machine Learning

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#scv-cs #Adaboost #Artificial Intelligence #Bagging #Boosting #Ensemble methods #Gradient boosting #Machine Learning #Meta-learning #Random Forest #Stacking #one-shot learning #few-shot learning #meta-learning

The ensemble techniques covered include bagging, boosting, stacking, and related algorithms such as random forest, Adaboost, and gradient boosting. One-shot and few-shot learning are also briefly introduced. An IEEE CS Silicon Valley Chapter Event.

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 covers some of the popular techniques of Meta-learning and explains why they generally work well.

To join IEEE Computer Society, Santa Clara Valley Chapter’s Dlist to hear about future programs, please visit https://r6.ieee.org/scv-cs

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The ensemble techniques covered include bagging, boosting, stacking, and related algorithms such as random forest, Adaboost, and gradient boosting. One-shot and few-shot learning are also briefly introduced. An IEEE CS Silicon Valley Chapter Event.

The word ‘meta’ indicates something beyond...

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