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Generalization (learning) Generalization is the concept that humans, other animals, and artificial neural networks use past learning in present situations of learning if the conditions in the situations are regarded as similar. [1] The learner uses generalized patterns, principles, and other similarities between past experiences and novel ...
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), refers to types of ...
e. In statistics, a generalized linear model (GLM) is a flexible generalization of ordinary linear regression. The GLM generalizes linear regression by allowing the linear model to be related to the response variable via a link function and by allowing the magnitude of the variance of each measurement to be a function of its predicted value.
Cartographic generalization is the process of selecting and representing information of a map in a way that adapts to the scale of the display medium of the map. In this way, every map has, to some extent, been generalized to match the criteria of display. This includes small cartographic scale maps, which cannot convey every detail of the real ...
A generative adversarial network (GAN) is a class of machine learning frameworks and a prominent framework for approaching generative AI. [ 1 ][ 2 ] The concept was initially developed by Ian Goodfellow and his colleagues in June 2014. [ 3 ] In a GAN, two neural networks contest with each other in the form of a zero-sum game, where one agent's ...
Zero-shot learning. Zero-shot learning (ZSL) is a problem setup in deep learning where, at test time, a learner observes samples from classes which were not observed during training, and needs to predict the class that they belong to. The name is a play on words based on the earlier concept of one-shot learning, in which classification can be ...
For supervised learning applications in machine learning and statistical learning theory, generalization error[1] (also known as the out-of-sample error[2] or the risk) is a measure of how accurately an algorithm is able to predict outcome values for previously unseen data. Because learning algorithms are evaluated on finite samples, the ...
Machine learningand data mining. Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent ought to take actions in a dynamic environment in order to maximize the cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside ...