Title
Automatic learning from positive data and negative counterexamples
Abstract
We introduce and study a model for learning in the limit by finite automata from positive data and negative counterexamples. The focus is on learning classes of languages with a membership problem computable by finite automata (so-called automatic classes). We show that, within the framework of our model, finite automata (automatic learners) can learn all automatic classes when memory of a learner is restricted by the size of the longest datum seen so far. We also study capabilities of automatic learners in our model with other restrictions on the memory and how the choice of negative counterexamples (arbitrary, or least, or the ones whose size is bounded by the longest positive datum seen so far) can impact automatic learnability.
Year
DOI
Venue
2017
10.1016/j.ic.2017.05.002
Inf. Comput.
Keywords
DocType
Volume
automatic learning,finite automaton,membership problem computable,longest positive datum,automatic class,automatic learner,negative counterexamples,automatic learnability,so-called automatic class,positive data,longest datum
Journal
255
ISSN
Citations 
PageRank 
0890-5401
0
0.34
References 
Authors
26
2
Name
Order
Citations
PageRank
Sanjay Jain11647177.87
Efim Kinber242144.95