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  1. Machine learning

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    Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit

  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

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

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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

  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. Outline of machine learning

    aimlexchange.com/search/wiki/page/Outline_of_machine_learning

    outline is provided as an overview of and topical guide to machine learning. Machine learning is a subfield of soft computing within computer science that

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