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  2. Supervised learning - Wikipedia

    en.wikipedia.org/wiki/Supervised_learning

    In supervised learning, the training data is labeled with the expected answers, while in unsupervised learning, the model identifies patterns or structures in unlabeled data. Supervised learning ( SL ) is a paradigm in machine learning where input objects (for example, a vector of predictor variables) and a desired output value (also known as a ...

  3. Reinforcement learning - Wikipedia

    en.wikipedia.org/wiki/Reinforcement_learning

    Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal. Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised ...

  4. Unsupervised learning - Wikipedia

    en.wikipedia.org/wiki/Unsupervised_learning

    Unsupervised learning is a framework in machine learning where, in contrast to supervised learning, algorithms learn patterns exclusively from unlabeled data. [1] Other frameworks in the spectrum of supervisions include weak- or semi-supervision , where a small portion of the data is tagged, and self-supervision .

  5. Weak supervision - Wikipedia

    en.wikipedia.org/wiki/Weak_supervision

    Semi-supervised learning combines this information to surpass the classification performance that can be obtained either by discarding the unlabeled data and doing supervised learning or by discarding the labels and doing unsupervised learning. Semi-supervised learning may refer to either transductive learning or inductive learning. [1]

  6. Statistical learning theory - Wikipedia

    en.wikipedia.org/wiki/Statistical_learning_theory

    The goals of learning are understanding and prediction. Learning falls into many categories, including supervised learning, unsupervised learning, online learning, and reinforcement learning. From the perspective of statistical learning theory, supervised learning is best understood. [4] Supervised learning involves learning from a training set ...

  7. Computational learning theory - Wikipedia

    en.wikipedia.org/wiki/Computational_learning_theory

    Theoretical results in machine learning mainly deal with a type of inductive learning called supervised learning. In supervised learning, an algorithm is given samples that are labeled in some useful way. For example, the samples might be descriptions of mushrooms, and the labels could be whether or not the mushrooms are edible.

  8. Neural network (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Neural_network_(machine...

    Machine learning is commonly separated into three main learning paradigms, supervised learning, [128] unsupervised learning [129] and reinforcement learning. [130] Each corresponds to a particular learning task.

  9. Learning rule - Wikipedia

    en.wikipedia.org/wiki/Learning_rule

    The learning rule is one of the factors which decides how fast or how accurately the neural network can be developed. Depending on the process to develop the network, there are three main paradigms of machine learning: supervised learning, unsupervised learning, and reinforcement learning.