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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 .
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. [1]
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.
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 ...
An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning).An autoencoder learns two functions: an encoding function that transforms the input data, and a decoding function that recreates the input data from the encoded representation.
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.
In the field of machine learning and specifically the problem of statistical classification, ... in unsupervised learning it is usually called a matching matrix. ...
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. In machine learning , supervised learning ( SL ) is a paradigm where a model is trained using input objects (e.g. a vector of predictor variables) and desired output ...