Design-Based Subsampling for Labeling Large and High-Dimensional Datasets — NSF Award to Purdue University (IN, $98,949)
Data labeling, the process of assigning labels or annotations to data points, is crucial in supervised machine learning for training models to make accurate predictions in various applications. Labels refer to the output variables that the machine learning model aims to predict or classify. For instance, in genetic and
| Award title | Design-Based Subsampling for Labeling Large and High-Dimensional Datasets |
|---|---|
| Award ID | 2413741 |
| Awardee | Purdue University |
| City | WEST LAFAYETTE |
| State | IN |
| Amount obligated | $98,949 |
| Principal investigator | Lin Wang |
| Program | STATISTICS |
| Start date | 09/01/2024 |
| Abstract | Data labeling, the process of assigning labels or annotations to data points, is crucial in supervised machine learning for training models to make accurate predictions in various applications. Labels refer to the output variables that the machine learning model aims to predict or classify. For instance, in genetic and genomic studies, labels may refer to traits or the presence of diseases, and accurate data labeling is essential for training models to understand the relationships between geneti |
| Source | NSF Awards |
$799/mo
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