Information about Test

  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. Active learning (machine learning)

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

  3. List of datasets for machine-learning research

    for machine-learning research and have been cited in peer-reviewed academic journals. Datasets are an integral part of the field of machine learning. Major

  4. 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

  5. Deep learning

    Deep learning (also known as deep structured learning or hierarchical learning or differential programming) is part of a broader family of machine learning

  6. Learning

    by humans, animals, and some machines; there is also evidence for some kind of learning in certain plants. Some learning is immediate, induced by a single

  7. 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

  8. Support-vector machine

    In machine learning, support-vector machines (SVMs, also support-vector networks) are supervised learning models with associated learning algorithms that

  9. Supervised learning

    Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. It infers

  10. Feature (machine learning)

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