Name
Abstract | ||
|---|---|---|
Machine learning approaches that utilize human experts combine domain experience with data to generate novel knowledge. Unfortunately, most methods either provide only a limited form of communication with the human expert and/or are overly reliant on the human expert to specify their knowledge upfront. Thus, the expert is unable to understand what the system could learn without their involvement. Allowing the learning algorithm to query the human expert in the most useful areas of the feature space takes full advantage of the data as well as the expert. We introduce active advice-seeking for relational domains. Relational logic allows for compact, but expressive interaction between the human expert and the learning algorithm. We demonstrate our algorithm empirically on several standard relational datasets. |
Year | Venue | Field |
|---|---|---|
2016 | ECML/PKDD | Feature vector,Active learning,Computer science,Artificial intelligence,Relational logic,Probabilistic logic,Machine learning |
DocType | Citations | PageRank |
Conference | 5 | 0.60 |
References | Authors | |
14 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Phillip Odom | 1 | 29 | 5.09 |
| Sriraam Natarajan | 2 | 482 | 49.32 |