Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 박승수 | * |
dc.date.accessioned | 2018-07-18T16:30:59Z | - |
dc.date.available | 2018-07-18T16:30:59Z | - |
dc.date.issued | 2006 | * |
dc.identifier.isbn | 3540454853 | * |
dc.identifier.isbn | 9783540454854 | * |
dc.identifier.issn | 0302-9743 | * |
dc.identifier.other | OAK-17732 | * |
dc.identifier.uri | https://dspace.ewha.ac.kr/handle/2015.oak/245063 | - |
dc.description.abstract | Recently, as the size of genetic knowledge grows faster, the automated analysis and systemization into high-throughput database has become a hot issue. In bioinformatics area, one of the essential tasks is to recognize and identify genomic entities and discover their relations from various sources. Generally, biological literatures containing ambiguous entities, are laid by decision boundaries. The purpose of this paper is to design and implement a classification system for improving performance in identifying entity problems. The system is based on reinforcement training and post-processing method and supplemented by data mining algorithms to enhance its performance. For experiments, we add some intentional noises to training data for testing the robustness and stability. The result shows significantly improved stability on training errors. © Springer-Verlag Berlin Heidelberg 2006. | * |
dc.language | English | * |
dc.title | Efficient classification method for complex biological literature using text and data mining combination | * |
dc.type | Conference Paper | * |
dc.relation.volume | 4224 LNCS | * |
dc.relation.index | SCOPUS | * |
dc.relation.startpage | 688 | * |
dc.relation.lastpage | 696 | * |
dc.relation.journaltitle | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | * |
dc.identifier.scopusid | 2-s2.0-33750554247 | * |
dc.author.google | Choi Y.J. | * |
dc.author.google | Park S.S. | * |
dc.contributor.scopusid | 박승수(7501833630) | * |
dc.date.modifydate | 20240322133123 | * |