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A machine learning model is a type of mathematical model that, after being "trained" on a given dataset, can be used to make predictions or classifications on new data.
1950s. Pioneering machine learning research is conducted using simple algorithms. 1960s. Bayesian methods are introduced for probabilistic inference in machine learning. [1] 1970s. ' AI winter ' caused by pessimism about machine learning effectiveness. 1980s.
Machine learning – a subfield of soft computing within computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. [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 ]
Glossary. v. t. e. Artificial general intelligence (AGI) is a theoretical type of artificial intelligence (AI) that falls within the lower and upper limits of human cognitive capabilities across a wide range of cognitive tasks. [1][2][3] This contrasts with narrow AI, which is limited to specific tasks. [4] Artificial superintelligence (ASI ...
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. The human user must possess knowledge/expertise in the problem domain, including the ability to consult/research authoritative sources ...
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 features ...