Learning from Multiple Sources of Inaccurate Data

Most theoretical models of inductive inference make the idealized assumption that the data available to a learner is from a single and accurate source. The subject of inaccuracies in data emanating from a single source has been addressed by several authors. The present paper argues in favor of a more realistic learning model in which data emanates from multiple sources, some or all of which may be inaccurate. Three kinds of inaccuracies are considered: spurious data (modeled as noisy texts), missing data (modeled as incomplete texts), and a mixture of spurious and missing data (modeled as imperfect texts). Motivated by the above argument, the present paper introduces and theoretically analyzes a number of inference criteria in which a learning machine is fed data from multiple sources, some of which may be infected with inaccuracies. The learning situation modeled is the identification in the limit of programs from graphs of computable functions. The main parameters of the investigatio...

• Type: Conference
• Year: 1992
• Where: AII
• Authors: Ganesh Baliga, Sanjay Jain, Arun Sharma
• Keywords: AII 1992, Final Hypothesis, Inaccurate Text, Single Inaccurate Text, Software Engineering