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  1. Machine learning

    aimlexchange.com/search/wiki/page/Machine_learning

    Machine learning (ML) is the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence

  2. Boosting (machine learning)

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    In machine learning, boosting is an ensemble meta-algorithm for primarily reducing bias, and also variance in supervised learning, and a family of machine

  3. Automated machine learning

    aimlexchange.com/search/wiki/page/Automated_machine_learning

    Automated machine learning (AutoML) is the process of automating the process of applying machine learning to real-world problems. AutoML covers the complete

  4. Adversarial machine learning

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    Adversarial machine learning is a technique employed in the field of machine learning which attempts to fool models through malicious input. This technique

  5. Feature (machine learning)

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    In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a phenomenon being observed. Choosing

  6. Active learning (machine learning)

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    Active learning is a special case of machine learning in which a learning algorithm can interactively query a user (or some other information source) to

  7. Weka (machine learning)

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    License, and the companion software to the book "Data Mining: Practical Machine Learning Tools and Techniques". Weka contains a collection of visualization

  8. Quantum machine learning

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    Quantum machine learning is an emerging interdisciplinary research area at the intersection of quantum physics and machine learning. The most common use

  9. Extreme learning machine

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    learning machines are feedforward neural networks for classification, regression, clustering, sparse approximation, compression and feature learning with

  10. Hyperparameter (machine learning)

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    In machine learning, a hyperparameter is a parameter whose value is used to control the learning process. By contrast, the values of other parameters

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