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  2. Algorithmic radicalization - Wikipedia

    en.wikipedia.org/wiki/Algorithmic_radicalization

    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 ...

  3. YouTube - Wikipedia

    en.wikipedia.org/wiki/YouTube

    The vast majority of videos on YouTube are free to view and supported by advertising. [64] In May 2013, YouTube introduced a trial scheme of 53 subscription channels with prices ranging from $0.99 to $6.99 a month. [313]

  4. BrownBoost - Wikipedia

    en.wikipedia.org/wiki/BrownBoost

    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]

  5. CatBoost - Wikipedia

    en.wikipedia.org/wiki/Catboost

    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]

  6. YouTube's algorithm pushes right-wing, explicit videos ... - AOL

    www.aol.com/news/youtubes-algorithm-pushes-wing...

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  7. Boosting (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Boosting_(machine_learning)

    Orange, a free data mining software suite, module Orange.ensemble Weka is a machine learning set of tools that offers variate implementations of boosting algorithms like AdaBoost and LogitBoost R package GBM (Generalized Boosted Regression Models) implements extensions to Freund and Schapire's AdaBoost algorithm and Friedman's gradient boosting ...

  8. XGBoost - Wikipedia

    en.wikipedia.org/wiki/XGBoost

    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.

  9. System Mechanic Software | 30-Day Free* Trial | AOL Products

    www.aol.com/products/utilities/system-mechanic

    Download System Mechanic to help repair and speed up your slow PC. Try it free* for 30 days now. ... and files and fixes problems to help keep your PC stable and issue free, saving you time, money ...