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The distributional learning theory or learning of probability distribution is a framework in computational learning theory.It has been proposed from Michael Kearns, Yishay Mansour, Dana Ron, Ronitt Rubinfeld, Robert Schapire and Linda Sellie in 1994 [1] and it was inspired from the PAC-framework introduced by Leslie Valiant.
Edwin Ray Guthrie (/ ˈ ɡ ʌ θ r i /; January 9, 1886 – April 23, 1959) was a behavioral psychologist who began his career in mathematics and philosophy.He spent most of his career at the University of Washington, where he became a full professor and then an emeritus professor in psychology.
Mastery learning is an educational philosophy first proposed by Bloom in 1968 [8] based on the premise that students must achieve a level of mastery (e.g., 90% on a knowledge test) in prerequisite knowledge before moving forward to learn subsequent information on a topic. [9]
One of Estes' most famous contributions to learning theory was stimulus-sampling theory, which conceives of learning as establishing associations to hypothetical stimulus elements that are randomly drawn from a pool of elements that characterize a particular learning situation. This theory predicted probability matching, which has been found in ...
Statistical learning theory is a framework for machine learning drawing from the fields of statistics and functional analysis. [ 1 ] [ 2 ] [ 3 ] Statistical learning theory deals with the statistical inference problem of finding a predictive function based on data.
The psychology of learning refers to theories and research on how individuals learn. There are many theories of learning. Some take on a more behaviorist approach which focuses on inputs and reinforcements. [1] [2] [3] Other approaches, such as neuroscience and social cognition, focus more on how the brain's organization and structure influence ...
In machine learning, a probabilistic classifier is a classifier that is able to predict, given an observation of an input, a probability distribution over a set of classes, rather than only outputting the most likely class that the observation should belong to.
Human contingency learning (HCL) is the observation that people tend to acquire knowledge based on whichever outcome has the highest probability of occurring from particular stimuli. In other words, individuals gather associations between a certain behaviour and a specific consequence.