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Titlebook: Algorithmic Learning Theory - ALT ‘92; Third Workshop, ALT Shuji Doshita,Koichi Furukawa,Toyaki Nishida Conference proceedings 1993 Spring

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Some improved sample complexity bounds in the probabilistic PAC learning model,lihood estimator. Second, our proof relies on the fact that only a linear number of examples are required in order to distinguish a true parametric model from a bad parametric model. The latter notion is apparently related to the notion of discrimination proposed and studied by Yamanishi, but the ex
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https://doi.org/10.1007/978-3-658-25453-7y seen in competitive hidden layer. In other words, explicit internal representation could be obtained. In two cases, multiple inhibitory connections were observed to be produced. Thus, entropy minimization method is completely equivalent to competitive learning approaches through mutual inhibition.
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https://doi.org/10.1007/978-3-662-37031-5d, (3)all phases of analogical reasoning are realized, and (4)multiple analogy is also realized. This problem solver is partly based on the observations on actual human problem solving processes with hypotheses formation. Hence, HPSA also tries to provide a cognitive simulation tool.
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Vorzüge des Betons als Pfahlbaustoffwork proposed by Tenenberg more flexible, still preserving DSP. For this purpose, the notion of a . is introduced. A partial predicate mapping corresponds to a refinement of inheritance hierarchy used in abstracting operators. After partitioning a given concrete operator set, a partial predicate map
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