Learning from multiple sources of inaccurate data

Ganesh Baliga, Sanjay Jain, Arun Sharma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Most theoretical studies of inductive inference model a situation involving a machine M learning its environment E on following lines. M, placed in E, receives data about E, and simultaneously conjectures a sequence of hypotheses. M is said to learn E just in case the sequence of hypotheses conjectured by M stabilizes to a final hypothesis which correctly represents E. The above model makes the idealized assumption that the data about E that M receives is from a single and accurate source. An argument is made in favor of a more realistic learning model which accounts for data emanating from multiple sources, some or all of which may be inaccurate. Motivated by this argument, the present paper introduces and theoretically analyzes a number of inference criteria in which a machine is fed data from multiple sources, some of which could be infected with inaccuracies. The main parameters of the investigation are the number of data sources, the number of faulty data sources, and the kind of inaccuracies.

Original languageEnglish (US)
Title of host publicationAnalogical and Inductive Inference - International Workshop AII 1992, Proceedings
EditorsKlaus P. Jantke
PublisherSpringer Verlag
Pages109-128
Number of pages20
ISBN (Print)9783540560043
DOIs
StatePublished - 1992
Event3rd International Workshop on Analogical and Inductive Inference, AII 1992 - Dagstuhl Castle, Germany
Duration: Oct 5 1992Oct 9 1992

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume642 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other3rd International Workshop on Analogical and Inductive Inference, AII 1992
CountryGermany
CityDagstuhl Castle
Period10/5/9210/9/92

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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