Two central criteria for data quality are consistency and accuracy. Inconsistencies and errors in a database often emerge as violations of integrity constraints. Given a dirty dat...
Data quality is critical for many information-intensive applications. One of the best opportunities to improve data quality is during entry. USHER provides a theoretical, data-dri...
Kuang Chen, Joseph M. Hellerstein, Tapan S. Parikh
Improved acoustic modeling can significantly decrease the error rate in large-vocabulary speech recognition. Our approach to the problem is twofold. We first propose a scheme that...
Classification is a core task in knowledge discovery and data mining, and there has been substantial research effort in developing sophisticated classification models. In a parall...
Ariel Fuxman, Anitha Kannan, Andrew B. Goldberg, R...
The standard model of supervised learning assumes that training and test data are drawn from the same underlying distribution. This paper explores an application in which a second...