| DOI | Resolve DOI: https://doi.org/10.1142/S0218001421510101 |
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| Author | Search for: Selvitella, Alessandro Maria; Search for: Valdés, Julio J.1 |
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| Affiliation | - National Research Council of Canada. Digital Technologies
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| Format | Text, Article |
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| Subject | Gamma Test; smooth modeling; K-Nearest Neighbours; a-priori estimates; variance of the noise; binary variables |
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| Abstract | In this paper, we discuss the problem of estimating the minimum error reachable by a regression model given a dataset, prior to learning. More specifically, we extend the Gamma Test estimates of the variance of the noise from the continuous case to the binary case. We give some heuristics for further possible extensions of the theory in the continuous case with the Láµ–-norm and conclude with some applications and simulations. From the point of view of machine learning, the result is relevant because it gives conditions under which there is no need to learn the model in order to predict the best possible performance. |
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| Publication date | 2021-08-23 |
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| Publisher | World Scientific |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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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 | b8d6fc0c-842f-407e-a7b6-59d5c4938eb8 |
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| Record created | 2021-12-13 |
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| Record modified | 2021-12-13 |
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