Name
Abstract | ||
|---|---|---|
As some cognitive research suggests, in the process of learning languages, in addition to overt explicit negative evidence, a child often receives covert explicit evidence in form of corrected or rephrased sentences. In this paper, we suggest one approach to formalization of overt and covert evidence within the framework of one-shot learners via subset and membership queries to a teacher (oracle). We compare and explore general capabilities of our models, as well as complexity advantages of learnabil- ity models of one type over models of other types, where complexity is measured in terms of number of queries. In particular, we establish that "correcting" positive ex- amples are sometimes more helpful to a learner than just negative (counter)examples and access to full positive data. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-540-75225-7_22 | Theor. Comput. Sci. |
Keywords | Field | DocType |
Inductive inference,Counterexample,Nearest positive example | Discrete mathematics,Inductive reasoning,Computer science,Oracle,Covert,Artificial intelligence,Counterexample,Cognition,Learnability,Machine learning | Journal |
Volume | Issue | ISSN |
410 | 27-29 | 0302-9743 |
Citations | PageRank | References |
3 | 0.40 | 21 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sanjay Jain | 1 | 1647 | 177.87 |
| Efim Kinber | 2 | 421 | 44.95 |