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BrownBoost is a boosting algorithm that may be robust to noisy datasets. BrownBoost is an adaptive version of the boost by majority algorithm. As is the case for all boosting algorithms, BrownBoost is used in conjunction with other machine learning methods. BrownBoost was introduced by Yoav Freund in 2001. [1]
Algorithmic radicalization is the concept that recommender algorithms on popular social media sites such as YouTube and Facebook drive users toward progressively more extreme content over time, leading to them developing radicalized extremist political views. Algorithms record user interactions, from likes/dislikes to amount of time spent on ...
While YouTube's revenue-sharing "Partner Program" made it possible to earn a substantial living as a video producer—its top five hundred partners each earning more than $100,000 annually [271] and its ten highest-earning channels grossing from $2.5 million to $12 million [272] —in 2012 CMU business editor characterized YouTube as "a free-to ...
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Trust – A recommender system is of little value for a user if the user does not trust the system. [106] Trust can be built by a recommender system by explaining how it generates recommendations, and why it recommends an item. Labelling – User satisfaction with recommendations may be influenced by the labeling of the recommendations. [107]
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The algorithm [ edit ] XGBoost works as Newton–Raphson in function space unlike gradient boosting that works as gradient descent in function space, a second order Taylor approximation is used in the loss function to make the connection to Newton–Raphson method.
CatBoost [6] is an open-source software library developed by Yandex.It provides a gradient boosting framework which, among other features, attempts to solve for categorical features using a permutation-driven alternative to the classical algorithm. [7]