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  1. Machine learning

    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)

    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

    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)

    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

    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)

    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

    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)

    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

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

  10. 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