Titlebook: Incremental Version-Space Merging: A General Framework for Concept Learning; Haym Hirsh Book 1990 Kluwer Academic Publishers 1990 Extensio

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Learning from Data with Bounded Inconsistency,a are said to be . with respect to the concept description language. In such cases no learner will be able to find a description classifying all instances correctly. In general, learning systems must generate reasonable results even when there is no concept definition consistent with all the data.

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Incremental Batch Learning,re generally, however, the information processed by incremental version-space merging need not correspond directly training data; as long as a piece of information can be converted into a version space of viable concept definitions it can be used by incremental version-space merging.

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Computational Complexity,n-space formation, and version-space intersection. The computational complexity of each of these is first discussed, followed by an analysis of the complexity of the overall incremental version-space merging method.

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Theoretical Underpinnings,under which an arbitrary set of concept definitions from a concept description language can be represented by boundary sets. This forms the new definition of version spaces. The chapter continues with an analysis of the conditions under which the intersection of two version spaces is a version space

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Book 1990ind all maxi­ mally consistent hypotheses, even in the presence of certain types of incon­ sistencies in the data. More generally, it provides a framework for integrat­ ing different types of constraints (e.g., training examples, prior knowledge) which allow the learner to reduce the set of hypothes

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0893-3405 es a framework for integrat­ ing different types of constraints (e.g., training examples, prior knowledge) which allow the learner to reduce the set of hypothes978-1-4612-8834-3978-1-4613-1557-5Series ISSN 0893-3405