label complexity
Country:
- North America > United States > Wisconsin > Dane County > Madison (0.14)
- North America > United States > California > San Diego County > San Diego (0.04)
TheLabelComplexityofActiveLearningfrom ObservationalData
In this problem, the learner is given observational data - a set of examples selected according to some policy along with their labels - as well as access to the policy that selects the examples, and the goal is to construct a classifier with high performance on an entire population, notjusttheobservational data distribution.
Country:
- Oceania > Australia > New South Wales > Sydney (0.04)
- North America > Canada > British Columbia > Metro Vancouver Regional District > Vancouver (0.04)
Country:
- Europe > Denmark > Capital Region > Copenhagen (0.04)
- Asia > Middle East > Jordan (0.04)
Technology:
Technology: Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (1.00)
Technology: Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (1.00)
Country:
- North America > United States > Washington > King County > Seattle (0.14)
- Europe > United Kingdom > England > Cambridgeshire > Cambridge (0.04)
Technology:
Country:
- Europe > United Kingdom > England > Cambridgeshire > Cambridge (0.04)
- North America > United States > Arizona (0.04)
- North America > Canada (0.04)
- Asia > Afghanistan > Parwan Province > Charikar (0.04)
Technology: Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (0.93)