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To summarize, this example illustrates a situation where B I+ h, but where B A D I- h The learned hypothesis in this case entails predictions that are not entailed by the domain theory alone The phrase knowledge-level learning is sometimes used to refer to this type of learning, in which the learned hypothesis entails predictions that go beyond those entailed by the domain theory The set of all predictions entailed by a set of assertions Y is often called the deductive closure of Y The key distinction here is that in knowledge-level learning the deductive closure of B is a proper subset of the deductive closure of B h A second example of knowledge-level analytical learning is provided by considering a type of assertions known as determinations, which have been explored in detail by Russell (1989) and others Determinations assert that some attribute of the instance is fully determined by certain other attributes, without specifying the exact nature of the dependence For example, consider learning the target concept "people who speak Portuguese," and imagine we are given as a domain theory the single determination assertion "the language spoken by a person is determined by their nationality" Taken alone, this domain theory does not enable us to classify any instances as positive or negative However, if we observe that "Joe, a 23year-old left-handed Brazilian, speaks Portuguese," then we can conclude from this positive example and the domain theory that "all Brazilians speak Portuguese" Both of these examples illustrate how deductive learning can produce output hypotheses that are not entailed by the domain theory alone In both of these cases, the output hypothesis h satisfies B A D I- h, but does not satisfy B I- h In both cases, the learner deduces a justified hypothesis that does not follow from either the domain theory alone or the training data alone. asp.net upc-a UPC-A . NET Control - UPC-A barcode generator with free . NET ...
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The UPC-A Code and the assignment of manufacturer ID numbers is controlled in the ... ASP . NET /Windows Forms/Reporting Services/Compact Framework ... As noted above, the practical applicability of the PROLOG-EBG algorithm is restricted by its requirement that the domain theory be correct and complete One important class of learning problems where this requirement is easily satisfied is learning to speed up complex search programs In fact, the largest scale attempts to apply explanation-based learning have addressed the problem of learning to control search, or what is sometimes called "speedup" learning For example, playing games such as chess involves searching through a vast space of possible moves and board positions to find the best move Many practical scheduling and optimization problems are easily formulated as large search problems, in which the task is to find some move toward the goal state In such problems the definitions of the legal search operators, together with the definition of the search objective, provide a complete and correct domain theory for learning search control knowledge
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