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Approximating the Recall[] matrix with the best-fit function to compute default recall in conditions of data scarcity. The approximation procedure uses a hill-climbing algorithm with parameters A, B, C, D displayed in the picture. Least squares deviation |
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Approximating the Recall[] matrix with the best-fit function to compute default recall in conditions of data scarcity. The approximation procedure uses a hill-climbing algorithm with parameters A, B, C, D displayed in the picture. Least squares deviation is obtained to asses the progress. The circles represent the Recall[] matrix at a chosen difficulty level. Their size corresponds with repetition cases investigated. The red surface is the best fit of the studied function to the Recall[] data. | <blockquote> | ||
'''''Figure:''' Approximating the [[Glossary:Recall_matrix|Recall[] matrix]] with the best-fit function to compute default recall in conditions of data scarcity. The approximation procedure uses a [https://en.wikipedia.org/wiki/Hill_climbing hill-climbing algorithm] with parameters '''A''', '''B''', '''C''', '''D''' displayed in the picture. Least squares deviation is obtained to asses the progress. The circles represent the [[Glossary:Recall_matrix|Recall[] matrix]] at a chosen [[Glossary:Difficulty|difficulty level]]. Their size corresponds with repetition cases investigated. The <span style="padding: 3px; color: #fff; background-color: red;">red surface</span> is the best fit of the studied function to the Recall[] data.'' | |||
</blockquote> | |||
Latest revision as of 13:37, 26 April 2016
Figure: Approximating the Recall[] matrix with the best-fit function to compute default recall in conditions of data scarcity. The approximation procedure uses a hill-climbing algorithm with parameters A, B, C, D displayed in the picture. Least squares deviation is obtained to asses the progress. The circles represent the Recall[] matrix at a chosen difficulty level. Their size corresponds with repetition cases investigated. The red surface is the best fit of the studied function to the Recall[] data.
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| current | 13:32, 26 April 2016 |  | 1,920 × 1,200 (334 KB) | SuperMemoHelp (talk | contribs) | Approximating the Recall[] matrix with the best-fit function to compute default recall in conditions of data scarcity. The approximation procedure uses a hill-climbing algorithm with parameters A, B, C, D displayed in the picture. Least squares deviation |
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