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  2. 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. Some researchers consider self ...

  3. Statistical learning theory - Wikipedia

    en.wikipedia.org/wiki/Statistical_learning_theory

    From the perspective of statistical learning theory, supervised learning is best understood. [4] Supervised learning involves learning from a training set of data. Every point in the training is an input–output pair, where the input maps to an output. The learning problem consists of inferring the function that maps between the input and the ...

  4. Supervised learning - Wikipedia

    en.wikipedia.org/wiki/Supervised_learning

    Active learning: Instead of assuming that all of the training examples are given at the start, active learning algorithms interactively collect new examples, typically by making queries to a human user. Often, the queries are based on unlabeled data, which is a scenario that combines semi-supervised learning with active learning.

  5. Machine learning: What’s the difference between supervised ...

    www.aol.com/machine-learning-difference-between...

    Machine learning, the subset of artificial intelligence that teaches computers to perform tasks through examples and experience, is a hot area of research and development. Many of the applications ...

  6. 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]

  7. Feature learning - Wikipedia

    en.wikipedia.org/wiki/Feature_learning

    Feature learning can be either supervised, unsupervised, or self-supervised: In supervised feature learning , features are learned using labeled input data. Labeled data includes input-label pairs where the input is given to the model, and it must produce the ground truth label as the output. [ 3 ]

  8. 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.

  9. Self-supervised learning - Wikipedia

    en.wikipedia.org/wiki/Self-supervised_learning

    Autoassociative self-supervised learning is a specific category of self-supervised learning where a neural network is trained to reproduce or reconstruct its own input data. [8] In other words, the model is tasked with learning a representation of the data that captures its essential features or structure, allowing it to regenerate the original ...