### Abstract

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 investigation are: the kind of inaccuracy, the total number of data sources, the number of faulty data sources which produce data within an acceptable bound, and the bound on the number of errors allowed in the final hypothesis learned by the machine. Sufficient conditions are determined under which, for the same kind of inaccuracy, for the same bound on the number of errors in the final hypothesis, and for the same bound on the number of inaccuracies, learning from multiple texts, some of which may be inaccurate, is equivalent to learning from a single inaccurate text. The general problem of determining when learning from multiple inaccurate texts is a restriction over learning from a single inaccurate text turns out to be combinatorially very complex. Significant partial results are provided for this problem. Several results are also provided about conditions under which the detrimental effects of multiple texts can be overcome by either allowing more errors in the final hypothesis or by reducing the number of inaccuracies in the texts. It is also shown that the usual hierarchies resulting from allowing extra errors in the final program (results in increased learning power) and allowing extra inaccuracies in the texts (results in decreased learning power) hold. Finally, it is demonstrated that in the context of learning from multiple inaccurate texts, spurious data is better than missing data, which in turn is better than a mixture of spurious and missing data.

Original language | English (US) |
---|---|

Pages (from-to) | 961-990 |

Number of pages | 30 |

Journal | SIAM Journal on Computing |

Volume | 26 |

Issue number | 4 |

DOIs | |

State | Published - Jan 1 1997 |

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### All Science Journal Classification (ASJC) codes

- Computer Science(all)
- Mathematics(all)

### Cite this

*SIAM Journal on Computing*,

*26*(4), 961-990. https://doi.org/10.1137/S0097539792239461

}

*SIAM Journal on Computing*, vol. 26, no. 4, pp. 961-990. https://doi.org/10.1137/S0097539792239461

**Learning from multiple sources of inaccurate data.** / Baliga, Ganesh; Jain, Sanjay; Sharma, Arun.

Research output: Contribution to journal › Article

TY - JOUR

T1 - Learning from multiple sources of inaccurate data

AU - Baliga, Ganesh

AU - Jain, Sanjay

AU - Sharma, Arun

PY - 1997/1/1

Y1 - 1997/1/1

N2 - 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 investigation are: the kind of inaccuracy, the total number of data sources, the number of faulty data sources which produce data within an acceptable bound, and the bound on the number of errors allowed in the final hypothesis learned by the machine. Sufficient conditions are determined under which, for the same kind of inaccuracy, for the same bound on the number of errors in the final hypothesis, and for the same bound on the number of inaccuracies, learning from multiple texts, some of which may be inaccurate, is equivalent to learning from a single inaccurate text. The general problem of determining when learning from multiple inaccurate texts is a restriction over learning from a single inaccurate text turns out to be combinatorially very complex. Significant partial results are provided for this problem. Several results are also provided about conditions under which the detrimental effects of multiple texts can be overcome by either allowing more errors in the final hypothesis or by reducing the number of inaccuracies in the texts. It is also shown that the usual hierarchies resulting from allowing extra errors in the final program (results in increased learning power) and allowing extra inaccuracies in the texts (results in decreased learning power) hold. Finally, it is demonstrated that in the context of learning from multiple inaccurate texts, spurious data is better than missing data, which in turn is better than a mixture of spurious and missing data.

AB - 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 investigation are: the kind of inaccuracy, the total number of data sources, the number of faulty data sources which produce data within an acceptable bound, and the bound on the number of errors allowed in the final hypothesis learned by the machine. Sufficient conditions are determined under which, for the same kind of inaccuracy, for the same bound on the number of errors in the final hypothesis, and for the same bound on the number of inaccuracies, learning from multiple texts, some of which may be inaccurate, is equivalent to learning from a single inaccurate text. The general problem of determining when learning from multiple inaccurate texts is a restriction over learning from a single inaccurate text turns out to be combinatorially very complex. Significant partial results are provided for this problem. Several results are also provided about conditions under which the detrimental effects of multiple texts can be overcome by either allowing more errors in the final hypothesis or by reducing the number of inaccuracies in the texts. It is also shown that the usual hierarchies resulting from allowing extra errors in the final program (results in increased learning power) and allowing extra inaccuracies in the texts (results in decreased learning power) hold. Finally, it is demonstrated that in the context of learning from multiple inaccurate texts, spurious data is better than missing data, which in turn is better than a mixture of spurious and missing data.

UR - http://www.scopus.com/inward/record.url?scp=0031207086&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0031207086&partnerID=8YFLogxK

U2 - 10.1137/S0097539792239461

DO - 10.1137/S0097539792239461

M3 - Article

AN - SCOPUS:0031207086

VL - 26

SP - 961

EP - 990

JO - SIAM Journal on Computing

JF - SIAM Journal on Computing

SN - 0097-5397

IS - 4

ER -