Titlebook: Learning Spaces; Interdisciplinary Ap Jean-Claude Falmagne,Jean-Paul Doignon Book 2011 Springer-Verlag Berlin Heidelberg 2011 ALEKS.Markov

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商议

Skill Maps, Labels and Filters,ning path’ or ‘gradation.’ This makes sense since, as suggested by our Examples in 1.4.1, 1.4.2 and 1.4.3, many of our results are potentially applicable to widely different fields. It must be realized, however, that our basic concepts are consistent with traditional explanatory features of psychome

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Entailments and the Maximal Mesh,real-life applications, we will typically assume this domain to be finite. The second step is then to construct a list of all the subsets of . that are feasible knowledge states, in the sense that anyone of them could conceivably occur in the population of reference. To secure such a list, we could

Acumen

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Descriptive and Assessment Languages*,typically be quite large. In such cases, it is impractical to describe a state by giving the full list of items that it contains. It is also unnecessary: because of the redundancy in many real-life knowledge structures., a state will often be characterizable by a relatively small set of features. Th

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Probabilistic Knowledge Structures, of a test. There are two ways in which probabilities must enter in a realistic model. For one, the knowledge states will certainly occur with different frequencies in the population of reference. It is thus reasonable to postulate the existence of a probability distribution on the collection of sta

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马赛克

A Markov Chain Procedure,on a finite Markov chain rather than on a Markov process with an uncountable set of Markov states. As a consequence, the procedure requires less storage and computation. It can thus be implemented on a small machine. A multi-step application of the procedure described in this chapter can be set up e

CHASE

Building a Knowledge Space,owledge state is one among possibly many states forming a knowledge structure for the topic. We now turn to the problem of building a knowledge structure in practice. In this chapter, we deal with the case of knowledge spaces, that is, union-closed knowledge structures. Methods for building learning