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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. Category:Supervised learning - Wikipedia

    en.wikipedia.org/wiki/Category:Supervised_learning

    Pages in category "Supervised learning" The following 6 pages are in this category, out of 6 total. This list may not reflect recent changes. ...

  4. Training, validation, and test data sets - Wikipedia

    en.wikipedia.org/wiki/Training,_validation,_and...

    A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]

  5. 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 ...

  6. 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 ]

  7. Active learning (machine learning) - Wikipedia

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

    Active learning is a special case of machine learning in which a learning algorithm can interactively query a human user (or some other information source), to label new data points with the desired outputs. The human user must possess knowledge/expertise in the problem domain, including the ability to consult/research authoritative sources ...

  8. Support vector machine - Wikipedia

    en.wikipedia.org/wiki/Support_vector_machine

    Transductive support vector machines extend SVMs in that they could also treat partially labeled data in semi-supervised learning by following the principles of transduction. Here, in addition to the training set D {\displaystyle {\mathcal {D}}} , the learner is also given a set

  9. Roblox - Wikipedia

    en.wikipedia.org/wiki/ROBLOX

    Roblox occasionally hosts real-life and virtual events. They have in the past hosted events such as BloxCon, which was a convention for ordinary players on the platform. [45] Roblox operates annual Easter egg hunts [51] and also hosts an annual event called the "Bloxy Awards", an awards ceremony that also functions as a fundraiser. The 2020 ...