Learning Languages in the Limit from Positive Information with Finitely Many Memory Changes

Autor:	Timo Kötzing, Karen Seidel
Rok vydání:	2021
Předmět:	Discrete mathematics Current (mathematics) Computer science Bounded function Geodetic datum Limit (mathematics) Inductive reasoning
Zdroj:	Lecture Notes in Computer Science ISBN: 9783030800482 CiE
DOI:	10.1007/978-3-030-80049-9_29
Popis:	We investigate learning collections of languages from texts by an inductive inference machine with access to the current datum and a bounded memory in form of states. Such a bounded memory states (\(\mathbf {BMS}\)) learner is considered successful in case it eventually settles on a correct hypothesis while exploiting only finitely many different states.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::f554919647a245cf1a95cb5ef8339e3d https://doi.org/10.1007/978-3-030-80049-9_29 Zobrazit plný text záznamu