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Author Gasarch, William I. ♦ Smith, Carl H.
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©1992
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Learning by example ♦ Learning via a teacher ♦ Omega automata
Abstract Traditional work in inductive inference has been to model a learner receiving data about a function $\textit{f}$ and trying to learn the function. The data is usually just the values $\textit{f}(0),$ $\textit{f}(1),….$ The scenario is modeled so that the learner is also allowed to ask questions about the data (e.g., ( ∀ 17 → $\textit{f}(\textit{&khgr;})$ = 0]?). An important parameter is the language that the lerner may use to formulate queries. We show that for most languages a learner can learn more by asking questions than by passively receiving data. Mathematical tools used include the solution to Hilbert's tenth problem, the decidability of Presuburger arithmetic, and ω-automata.
ISSN 00045411
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 1992-07-01
Publisher Place New York
e-ISSN 1557735X
Journal Journal of the ACM (JACM)
Volume Number 39
Issue Number 3
Page Count 26
Starting Page 649
Ending Page 674


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Source: ACM Digital Library