One-Shot Learners Using Negative Counterexamples and Nearest Positive Examples
Document Type
Conference Proceeding
Publication Date
2007
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 learnability 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 examples give sometimes more power to a learner than just negative (counter)examples and access to full positive data.
DOI
10.1007/978-3-540-75225-7_22
Recommended Citation
Jain, S., & Kinber, E. (2007). One-shot learners using negative counterexamples and nearest positive examples. In Hutter, M., Servedio, R. A., Takimoto, E. (Eds.), Algorithmic learning theory ICALT 2007. Lecture Notes in Computer Science, vol 64754. Springer. Doi.org/10.1007/978-3-540-75225-7_22
Comments
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4754).
International Conference on Algorithmic Learning Theory.
ISBN: 9783540752240