TY - GEN
T1 - Learning from multiple sources of inaccurate data
AU - Baliga, Ganesh
AU - Jain, Sanjay
AU - Sharma, Arun
N1 - Publisher Copyright:
© 1992, Springer Verlag. All rights reserved.
PY - 1992
Y1 - 1992
N2 - 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.
AB - 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.
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U2 - 10.1007/3-540-56004-1_8
DO - 10.1007/3-540-56004-1_8
M3 - Conference contribution
AN - SCOPUS:0346963432
SN - 9783540560043
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 109
EP - 128
BT - Analogical and Inductive Inference - International Workshop AII 1992, Proceedings
A2 - Jantke, Klaus P.
PB - Springer Verlag
T2 - 3rd International Workshop on Analogical and Inductive Inference, AII 1992
Y2 - 5 October 1992 through 9 October 1992
ER -