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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 and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases).
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).
MuZero was viewed as a significant advancement over AlphaZero, and a generalizable step forward in unsupervised learning techniques. [8] [9] The work was seen as advancing understanding of how to compose systems from smaller components, a systems-level development more than a pure machine-learning development. [10]
The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. These parameter-estimates are then ...
Machine learning (ML) is a subfield of artificial intelligence within computer science that evolved from the study of pattern recognition and computational learning theory. [1] In 1959, Arthur Samuel defined machine learning as a "field of study that gives computers the ability to learn without being explicitly programmed". [ 2 ]
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.
Weak supervision (also known as semi-supervised learning) is a paradigm in machine learning, ... distance metric, or kernel for the data in an unsupervised first step ...