DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 신하용 | - |
dc.contributor.author | Kazmina, Anastasiia | - |
dc.contributor.author | 아나스타샤 | - |
dc.date.accessioned | 2024-07-25T19:31:02Z | - |
dc.date.available | 2024-07-25T19:31:02Z | - |
dc.date.issued | 2023 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1045803&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/320615 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 산업및시스템공학과, 2023.8,[iii, 20 p. :] | - |
dc.description.abstract | Travel Salesman Problem is the most popular combinatorial optimization problem. Although numerous heuristics and machine learning approaches were proposed, training a value function for TSP remains a challenging task. In this research, the main focus is shifted from finding a capable model to the wise choice of instances to label using active learning. Two selection criteria based on one-step look-ahead and novelty for choosing the best instance to label are proposed. In addition, a metric to evaluate performance in active learning settings is introduced. The experimental results demonstrate that applying active learning gives a significant advantage over passive learning. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | 능동 학습▼a외판원 순회▼a가치 함수▼a한 단계 전방 알고리즘▼a멀티 헤드 어텐션 | - |
dc.subject | Active Learning▼aTSP▼aValue Function▼aOne-Step Look-Ahead Heuristic▼aMHA | - |
dc.title | Active learning in estimating optimal tour length for the travelling salesman problem | - |
dc.title.alternative | 능동학습을 이용한 외판원 순회 거리 예측 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :산업및시스템공학과, | - |
dc.contributor.alternativeauthor | Shin, Hayong | - |
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