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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]
This template is used on approximately 650,000 pages, or roughly 1% of all pages. To avoid major disruption and server load, any changes should be tested in the template's /sandbox or /testcases subpages, or in your own user subpage .
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
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.
Evidence-based library and information practice (EBLIP) or evidence-based librarianship (EBL) is the use of evidence-based practices (EBP) in the field of library and information science (LIS). This means that all practical decisions made within LIS should 1) be based on research studies and 2) that these research studies are selected and ...
Advanced training is one of EMBL's five core missions. [18] Over the years, the Laboratory has established a number of training activities, of which the EMBL International PhD Programme (EIPP) is the flagship – it has a student body of about 200, and since 1997 has had the right to award its own degree, although currently students receive ...
StarOffice supported the OpenOffice.org XML file format, as well as the OpenDocument standard, and could generate PDF and Flash formats. It included templates, a macro recorder, and a software development kit (SDK). The software originated in 1985 as StarWriter by Star Division, which marketed the suite with some success, primarily in Europe.
Assign a new and additional task to an existing resource. Less affluent cultures are more likely to adopt the Task Unification mindset. For example, the Bedouins use camels for a number of different tasks: transportation, currency, milk, skin for tents, shade, protection from the wind, burning feces for fuel.