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Explanation-based learning (EBL) is a form of machine learning that exploits a very strong, or even perfect, domain theory (i.e. a formal theory of an application domain akin to a domain model in ontology engineering, not to be confused with Scott's domain theory) in order to make generalizations or form concepts from training examples. [1]
Call of the Shofar (founded by Simcha Frischling) [citation needed]; Context International [2] [9] (previously Context Associated, founded by Randy Revell, who had worked with Mind Dynamics)
Major advances in this field can result from advances in learning algorithms (such as deep learning), computer hardware, and, less-intuitively, the availability of high-quality training datasets. [1] High-quality labeled training datasets for supervised and semi-supervised machine learning algorithms are usually difficult and expensive to ...
Example of problem/project based learning versus reading cover to cover. The problem/project based learner may memorize a smaller amount of total information due to spending time searching for the optimal information across various sources, but will likely learn more useful items for real world scenarios, and will likely be better at knowing where to find information when needed.
Active learning: Instead of assuming that all of the training examples are given at the start, active learning algorithms interactively collect new examples, typically by making queries to a human user. Often, the queries are based on unlabeled data, which is a scenario that combines semi-supervised learning with active learning.
Input List of examples and predicate to be learned Output A set of first-order Horn clauses FOIL(Pred, Pos, Neg) Let Pos be the positive examples Let Pred be the predicate to be learned Until Pos is empty do: Let Neg be the negative examples Set Body to empty Call LearnClauseBody Add Pred ← Body to the rule Remove from Pos all examples which ...
An example of Electron beam lithograph setup. Electron-beam lithography (often abbreviated as e-beam lithography or EBL) is the practice of scanning a focused beam of electrons to draw custom shapes on a surface covered with an electron-sensitive film called a resist (exposing). [1]
The worked-example effect is a learning effect predicted by cognitive load theory. [1] [full citation needed] Specifically, it refers to improved learning observed when worked examples are used as part of instruction, compared to other instructional techniques such as problem-solving [2] [page needed] and discovery learning.