Affiliations: Department of Computer Science, The University of Maryland, College Park Maryland, 20742 U.S.A. | Department of Computer and Information Science, The University of Delaware, Newark, DE 19716 | IBM Application Solutions Division, Bethesda, Maryland, U.S.A. | Department of Computer Science, The University of Maryland, College Park Maryland, 20742 U.S.A. | Research Institute of Fundamental Information Science, Kyushu University 33, Fukuoka 812 Japan
Note: [] Supported, in part, by National Science Foundation Grants CCR 8803641 and CCR 9020079.
Note: [] On leave from The Institute of Mathematics and Computer Science, University of Latvia.
Note: [] Supported, in part, by National Science Foundation Grants CCR 8701104 and CCR 9020079.
Note: [] On leave from the Institute of Theoretical Computer Science, TH Darstadt, 64283 Darmstadt, Germany.
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.
