Learning Via Queries With Teams And Anomalies

Authors:
William I. Gasarch;Efim B. Kinber;Mark G. Pleszkoch;Carl H. Smith;Thomas Zeugmann
Affiliations:
(Supported, in part, by National Science Foundation Grants CCR 8803641 and CCR 9020079) Department of Computer Science, The University of Maryland, College Park Maryland, 20742 U.S.A.;(On leave from The Institute of Mathematics and Computer Science, University of Latvia) Department of Computer and Information Science, The University of Delaware, Newark, DE 19716;IBM Application Solutions Division, Bethesda, Maryland, U.S.A.;(Supported, in part, by National Science Foundation Grants CCR 8701104 and CCR 9020079) Department of Computer Science, The University of Maryland, College Park Maryland, 20742 U.S.A.;(On leave from the Institute of Theoretical Computer Science, TH Darstadt, 64283 Darmstadt, Germany) Research Institute of Fundamental Information Science, Kyushu University 33, Fukuoka 812 Japan
Venue:
Fundamenta Informaticae
Year:
1995

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Abstract

Most work in the field of inductive inference regards the learning machine to be a passive recipient of data. In a prior paper the passive approach was compared to an active form of learning where the machine is allowed to ask questions. In this paper we continue the study of machines that ask questions by comparing such machines to teams of passive machines. This yields, via work of Pitt and Smith, a comparison of active learning with probabilistic learning. Also considered are query inference machines that learn an approximation of what is desired. The approximation differs from the desired result in finitely many anomalous places.