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Supervised learning. 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 human-labeled supervisory signal) train a model. The training data is processed, building a function that maps new data to expected output values. [ 1]
v. t. e. In machine learning, a neural network (also artificial neural network or neural net, abbreviated ANN or NN) is a model inspired by the structure and function of biological neural networks in animal brains. [ 1][ 2] An ANN consists of connected units or nodes called artificial neurons, which loosely model the neurons in the brain.
Statistical learning theory is a framework for machine learning drawing from the fields of statistics and functional analysis. [1] [2] [3] Statistical learning theory deals with the statistical inference problem of finding a predictive function based on data. Statistical learning theory has led to successful applications in fields such as ...
In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a phenomenon. [ 1] Choosing informative, discriminating and independent features is a crucial element of effective algorithms in pattern recognition, classification and regression. Features are usually numeric, but structural ...
t. e. Self-supervised learning ( SSL) is a paradigm in machine learning where a model is trained on a task using the data itself to generate supervisory signals, rather than relying on external labels provided by humans. In the context of neural networks, self-supervised learning aims to leverage inherent structures or relationships within the ...
In machine learning, feature learning or representation learning[ 2] 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 ...
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 .
Apache 2.0. Website. arxiv .org /abs /1810 .04805. Bidirectional Encoder Representations from Transformers ( BERT) is a language model introduced in October 2018 by researchers at Google. [ 1][ 2] It learned by self-supervised learning to represent text as a sequence of vectors. It had the transformer encoder architecture.
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