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Fingerprint Dive into the research topics where Seoung Bum Kim is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

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Control Charts Mathematics
Multivariate Control Charts Mathematics
Feature extraction Engineering & Materials Science
Data mining Engineering & Materials Science
Statistical process control Engineering & Materials Science
Process monitoring Engineering & Materials Science
Clustering algorithms Engineering & Materials Science
Monitoring Engineering & Materials Science

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Research Output 2003 2020

  • 1159 Citations
  • 20 h-Index
  • 94 Article
  • 18 Conference contribution

Outer-Points shaver: Robust graph-based clustering via node cutting

Kim, Y., Do, H. & Kim, S. B., 2020 Jan 1, In : Pattern Recognition. 97, 107001.

Research output: Contribution to journalArticle

Active semi-supervised learning with multiple complementary information

Park, S. H. & Kim, S. B., 2019 Jul 15, In : Expert Systems with Applications. 126, p. 30-40 11 p.

Research output: Contribution to journalArticle

Supervised learning
Design of experiments
Clustering algorithms
Labeling
Learning algorithms

An Ensemble Feature Ranking Algorithm for Clustering Analysis

Yu, J., Zhong, H. & Kim, S. B., 2019 Jan 1, In : Journal of Classification.

Research output: Contribution to journalArticle

Clustering Analysis
Cluster Analysis
ranking
Ranking
Ensemble

Iterative two-stage hybrid algorithm for the vehicle lifter location problem in semiconductor manufacturing

Lee, S., Kahng, H. G., Cheong, T. S. & Kim, S. B., 2019 Apr 1, In : Journal of Manufacturing Systems. 51, p. 106-119 14 p.

Research output: Contribution to journalArticle

Materials handling
Semiconductor materials
Automation
Hoists
Fabrication

Multi-agent reinforcement learning with approximate model learning for competitive games

Park, Y. J., Cho, Y. S. & Kim, S. B., 2019 Jan 1, In : PloS one. 14, 9, e0222215.

Research output: Contribution to journalArticle

Open Access
Reinforcement learning
learning
Learning
Reward
Recurrent neural networks