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Central applications of unsupervised machine learning include clustering, dimensionality reduction, [7] and density estimation. [ 51 ] Cluster analysis is the assignment of a set of observations into subsets (called clusters ) so that observations within the same cluster are similar according to one or more predesignated criteria, while ...
Theoretical results in machine learning mainly deal with a type of inductive learning called supervised learning.In supervised learning, an algorithm is given samples that are labeled in some useful way.
Various countries are deploying AI military applications. [86] The main applications enhance command and control, communications, sensors, integration and interoperability. [87] Research is targeting intelligence collection and analysis, logistics, cyber operations, information operations, and semiautonomous and autonomous vehicles. [86]
Classification is very common for machine learning applications. In facial recognition, for instance, a picture of a person's face would be the input, and the output label would be that person's name. The input would be represented by a large multidimensional vector whose elements represent pixels in the picture.
Active learning is a special case of machine learning in which a learning algorithm can interactively query a human user (or some other information source), to label new data points with the desired outputs.
multiagent/distributed reinforcement learning is a topic of interest. Applications are expanding. [34] occupant-centric control; optimization of computing resources [35] [36] [37] partial information (e.g., using predictive state representation) reward function based on maximising novel information [38] [39] [40]
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 ]
Classically, an inductive model splits into a training and an application phase: the model parameters are estimated in the training phase, and the learned model is applied an arbitrary many times in the application phase. In the asymptotic limit of the number of applications, this splitting of phases is also present with quantum resources. [106]