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The National Council Licensure Examination (NCLEX) is a nationwide examination for the licensing of nurses in the United States, Canada, and Australia since 1982, 2015, and 2020, respectively. [2] [3] There are two types: the NCLEX-RN and the NCLEX-PN. After graduating from a school of nursing, one takes the NCLEX exam to receive a nursing license.
KNA's test preparation services are branded through Kaplan Test Prep, Manhattan Prep and Barron's Educational Series. In total, Kaplan's test prep offerings prepare students for more than 233 standardized tests, most of which are U.S. focused. Kaplan Publishing focuses on print and digital test preparation and reference resources.
Subsequently, Kaplan and David P. Norton included anonymous details of this balanced scorecard design in a 1992 article. [5] Although Kaplan and Norton's article was not the only paper on the topic published in early 1992, [10] it was a popular success, and was quickly followed by a second in 1993. [11]
A decision tree is a decision support recursive partitioning structure that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains conditional control statements.
The first diagrams of this type appeared in the early 1990s, and the idea of using this type of diagram to help document Balanced Scorecard was discussed in a paper by Robert S. Kaplan and David P. Norton in 1996. [1] The strategy map idea featured in several books and articles during the late 1990s by Robert S. Kaplan and David P. Norton.
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Decision tables are a concise visual representation for specifying which actions to perform depending on given conditions. Decision table is the term used for a Control table or State-transition table in the field of Business process modeling ; they are usually formatted as the transpose of the way they are formatted in Software engineering .
The problem of learning an optimal decision tree is known to be NP-complete under several aspects of optimality and even for simple concepts. [34] [35] Consequently, practical decision-tree learning algorithms are based on heuristics such as the greedy algorithm where locally optimal decisions are made at each node. Such algorithms cannot ...