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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)
These are companies totally or significantly owned (directly or indirectly) by their employees. [1] Employee ownership takes different forms and one form may predominate in a particular country. For example, in the U.S. over 5,700 of the roughly 6,400 employee-owned companies have an Employee Stock Ownership Plan (ESOP). [2]
Executing the training on at the job location, rather than the classroom, creates a stress-free environment for the employees. On-the-job training is the most popular method of training not only in the United States but in most of the developed countries, such as the United Kingdom, Canada, Australia, etc. Its effectiveness is based on the use ...
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]
Examples of instance-based learning algorithms are the k-nearest neighbors algorithm, kernel machines and RBF networks. [2]: ch. 8 These store (a subset of) their training set; when predicting a value/class for a new instance, they compute distances or similarities between this instance and the training instances to make a decision.
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