| Download | - View accepted manuscript: A Theory of Cross-Validation Error (PDF, 642 KiB)
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| Author | Search for: Turney, Peter1 |
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| Affiliation | - National Research Council of Canada. NRC Institute for Information Technology
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| Format | Text, Article |
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| Subject | cross validation; curve fitting; AIC; bias; variance; ajustement de courbe; AIC; biais; variance |
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| Abstract | This paper presents a theory of error in cross-validation testing of algorithms for predicting real-valued attributes. The theory justifies the claim that predictingreal-valued attributes requires balancing the conflicting demands of simplicity and accuracy. Furthermore, the theory indicates precisely how these conflicting demands must be balanced, in order to minimize cross-validation error. A general theory is presented, then it is developed in detail for linear regression and instance-based learning. |
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| Publication date | 1994 |
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| In | |
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| Language | English |
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| NRC number | NRCC 35072 |
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| NPARC number | 8913080 |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 68cc9d13-ee31-4e00-aa3c-087c3f3471de |
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| Record created | 2009-04-22 |
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| Record modified | 2020-04-27 |
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