mislabeled example
Country:
- North America > United States > California > Santa Clara County > Palo Alto (0.04)
- North America > Canada (0.04)
Technology:
InteractiveLabelCleaningwith Example-basedExplanations
We tackle sequential learning under label noise in applications where a human supervisor can be queried to relabel suspicious examples.
Country:
- Europe > Italy > Trentino-Alto Adige/Südtirol > Trentino Province > Trento (0.05)
- North America > United States > New York (0.04)
Characterizing Datapoints via Second-Split Forgetting Supplementary Material A Theoretical Results A.1 Preliminaries Let w 2 R
We assume the sample complexity required to estimate the distribution as a proxy for the complexity of the distribution. We make these assumptions to simplify the theoretical exposition. However, our results can be observed even after relaxing them at the expense of more book-keeping. Based on Chatterji and Long [ 9 ], we make the following assumptions about the problem setup: (A.1) The labels are reversed for mislabeled examples.
Country:
- North America > United States > Pennsylvania > Allegheny County > Pittsburgh (0.04)
- North America > United States > Washington > King County > Seattle (0.04)
Technology:
- Information Technology > Artificial Intelligence > Natural Language (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks (0.96)
- Information Technology > Data Science (0.93)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (0.68)