Version Space Learning with DNA Molecules

Autor:	Hee-Woong Lim, Young Gyu Chai, Byoung-Tak Zhang, Ji-Eun Yun, Suk-In Yoo, Haeman Jang
Rok vydání:	2003
Předmět:	chemistry.chemical_compound chemistry Exponential growth Magnetic bead Concept learning Version space Space (mathematics) Algorithm DNA Mathematics Conjunction (grammar) Single strand
Zdroj:	DNA Computing ISBN: 9783540005315 DNA
DOI:	10.1007/3-540-36440-4_13
Popis:	Version space is used in inductive concept learning to represent the hypothesis space where the goal concept is expressed as a conjunction of attribute values. The size of the version space increases exponentially with the number of attributes. We present an efficient method for representing the version space with DNA molecules and demonstrate its effectiveness by experimental results. Primitive operations to maintain a version space are derived and their DNA implementations are described. We also propose a novel method for robust decision-making that exploits the huge number of DNA molecules representing the version space.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::d2f71a34b9030ec6d625a8a2c15ddfe6 https://doi.org/10.1007/3-540-36440-4_13 Zobrazit plný text záznamu