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этимология

Что (кто) такое unsupervised$88506$ - определение

MACHINE LEARNING TECHNIQUE

Unsupervised approach; Unsupervised classification; Unsupervised machine learning; Self-taught AI; Self-taught artificial intelligence; Algorithms for unsupervised learning

Unsupervised learning

Unsupervised learning is a type of algorithm that learns patterns from untagged data. The hope is that through mimicry, which is an important mode of learning in people, the machine is forced to build a compact internal representation of its world and then generate imaginative content from it.

https://en.wikipedia.org/wiki/Unsupervised_learning

Stepwise regression

METHOD OF STATISTICAL FACTOR ANALYSIS

Forward selection; Backward elimination; Unsupervised forward selection; Unsupervised Forward Selection; Stagewise regression; Step-wise regression; Stepwise Regression

In statistics, stepwise regression is a method of fitting regression models in which the choice of predictive variables is carried out by an automatic procedure.Efroymson,M.

https://en.wikipedia.org/wiki/Stepwise_regression

Feature learning

A SET OF TECHNIQUES THAT LEARN A FEATURE: A TRANSFORMATION OF RAW DATA INPUT TO A REPRESENTATION THAT CAN BE EFFECTIVELY EXPLOITED IN MACHINE LEARNING TASKS

Learning representation; Representation learning; Unsupervised feature learning; Supervised feature learning

In machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task.

https://en.wikipedia.org/wiki/Feature_learning

Википедия

Unsupervised learning

Unsupervised learning is a type of algorithm that learns patterns from untagged data. The goal is that through mimicry, which is an important mode of learning in people, the machine is forced to build a concise representation of its world and then generate imaginative content from it.

In contrast to supervised learning where data is tagged by an expert, e.g. tagged as a "ball" or "fish", unsupervised methods exhibit self-organization that captures patterns as probability densities or a combination of neural feature preferences encoded in the machine's weights and activations. The other levels in the supervision spectrum are reinforcement learning where the machine is given only a numerical performance score as guidance, and semi-supervised learning where a small portion of the data is tagged.

https://en.wikipedia.org/wiki/Unsupervised learning