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- W84828986 abstract "This chapter discusses learning concepts on countable domains. A concept is a subset of the objects in a predefined domain. Concept learning is the process in which a learner constructs a good approximation to an unknown concept, given a small number of examples and some prior information on the concept to be learned. This prior information is also called the learner's prior knowledge or bias. A concept is a subset of the objects in a predefined domain. There are other possible forms that the prior information may take. For instance, the prior information might tell something about the probability of seeing each object in the domain, thereby indicating the importance of correctly classifying each object with respect to the unknown concept. First, a concept is a good approximation to the target concept if it is likely to agree with it on a randomly chosen element of the domain. Second, it is likely, but not certain, that the learner will learn a good approximation to the target concept. With high probability, this is to be a good approximation. A concept is a good approximation to the target concept if it is likely to agree with the target concept on a word chosen at random according to the distribution." @default.
- W84828986 created "2016-06-24" @default.
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- W84828986 date "1991-01-01" @default.
- W84828986 modified "2023-09-24" @default.
- W84828986 title "Learning Concepts on Countable Domains" @default.
- W84828986 doi "https://doi.org/10.1016/b978-0-08-051053-8.50005-3" @default.
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