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Knowledge based learning, an example

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LanguageTerm (authority = ISO 639-3:2007); (type = text)
English
Genre (authority = RULIB-FS)
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Genre (authority = marcgt)
technical report
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application/pdf
Extent
1 online resource (39 pages) : illustrations
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Technical report DCS-TR-90
Name (type = corporate); (authority = RutgersOrg-School)
NamePart
School of Arts and Sciences (SAS) (New Brunswick)
Name (type = corporate); (authority = RutgersOrg-Department)
NamePart
Computer Science (New Brunswick)
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Text
TitleInfo
Title
Knowledge based learning, an example
Abstract (type = abstract)
Domain knowledge is viewed here as consisting of two parts: A domain theory, TH[D], for a domain D, which can be used to decide whether a given instance, S,belongs to the domain D or not, and domain heuristics, H[D], which can be used by a machine to solve problems in the domain D using the theory TH[D]. A machine that can build theories TH[D] and learn H[D] from experience of using TH[D] to solve problems in D is called a Knowledge Based Learning machine. The theory formation aspects of a knowledge based learning machine are illustrated here with a simple example. It is argued that such a machine should be endowed with the right kinds of a priori "biases" in order to be able to examine given instances of a domain and formulate domain theories. The example is used here to present a typical set of generally applicable "biases" of a knowledge based learning machine.
Name (type = personal)
NamePart (type = family)
Srinivasan
NamePart (type = given)
Chitoor V.
Affiliation
Computer Science (New Brunswick)
Role
RoleTerm (type = text); (authority = marcrt)
author
OriginInfo
DateCreated (encoding = w3cdtf); (qualifier = exact); (keyDate = yes)
1980-05
RelatedItem (type = host)
TitleInfo
Title
Computer Science (New Brunswick)
Identifier (type = local)
rucore21032500001
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier (type = doi)
doi:10.7282/t3-cxyx-1r88
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Technical

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Document
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