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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 .
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]
Bolton & Hand define statistical data analysis as either supervised or unsupervised methods. [15] Supervised learning methods require that rules are defined within the system to establish what is expected or unexpected behavior. Unsupervised learning methods review data in comparison to the norm and detect statistical outliers. Supervised ...
A self-organizing map (SOM) or self-organizing feature map (SOFM) is an unsupervised machine learning technique used to produce a low-dimensional (typically two-dimensional) representation of a higher-dimensional data set while preserving the topological structure of the data.
Quizlet made its first acquisition in March 2021, with the purchase of Slader, which offered detailed explanations of textbook concepts and practice problems, and eventually incorporated it into its paid platform, Quizlet Plus. [20] [21] [22] In November 2022, Quizlet announced a new CEO, Lex Bayer, the former CEO of Starship Technologies. [23]
Weak supervision (also known as semi-supervised learning) is a paradigm in machine learning, the relevance and notability of which increased with the advent of large language models due to large amount of data required to train them.
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Download QR code; Print/export Download as PDF; ... Pages in category "Unsupervised learning" The following 27 pages are in this category, out of 27 total.