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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 ]
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.
Feature engineering is a preprocessing step in supervised machine learning and statistical modeling [1] which transforms raw data into a more effective set of inputs. Each input comprises several attributes, known as features.
Feature learning can be either supervised or unsupervised. In supervised feature learning, features are learned using labeled input data. Examples include artificial neural networks, multilayer perceptrons, and supervised dictionary learning. In unsupervised feature learning, features are learned with unlabeled input data.
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]
In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a data set. [1] Choosing informative, discriminating, and independent features is crucial to produce effective algorithms for pattern recognition, classification, and regression tasks.
k-means clustering has been used as a feature learning (or dictionary learning) step, in either supervised learning or unsupervised learning. [53] The basic approach is first to train a k-means clustering representation, using the input training data (which need not be labelled).
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 ...