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Advanced Distributed Learning. The Advanced Distributed Learning (ADL) Initiative is a US government program that conducts research and development on distributed learning and coordinates related efforts broadly across public and private organizations. ADL reports to the Defense Human Resources Activity (DHRA), under the Director, DHRA.
Distributed learning is an instructional model that allows instructor, students, and content to be located in different, noncentralized locations so that instruction and learning can occur independent of time and place. The distributed learning model can be used in combination with traditional classroom-based courses and traditional distance ...
Distributed Artificial Intelligence (DAI) is an approach to solving complex learning, planning, and decision-making problems. It is embarrassingly parallel, thus able to exploit large scale computation and spatial distribution of computing resources. These properties allow it to solve problems that require the processing of very large data sets.
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. [2]
Distributed practice. Distributed practice (also known as spaced repetition, the spacing effect, or spaced practice) is a learning strategy, where practice is broken up into a number of short sessions over a longer period of time. Humans and other animals learn items in a list more effectively when they are studied in several sessions spread ...
Distributional semantics[1] is a research area that develops and studies theories and methods for quantifying and categorizing semantic similarities between linguistic items based on their distributional properties in large samples of language data. The basic idea of distributional semantics can be summed up in the so-called distributional ...
v. t. e. Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning. In this formalism, a classification or regression decision tree is used as a predictive model to draw conclusions about a set of observations. Tree models where the target variable can take a discrete set of values are called ...
Federated learning (also known as collaborative learning) is a sub-field of machine learning focusing on settings in which multiple entities (often referred to as clients) collaboratively train a model while ensuring that their data remains decentralized. [1] This stands in contrast to machine learning settings in which data is centrally stored ...