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

  • 1 Similar Profiles
Authentication Engineering & Materials Science
Semiconductor materials Engineering & Materials Science
Classifiers Engineering & Materials Science
Supervised learning Engineering & Materials Science
Learning systems Engineering & Materials Science
Labels Engineering & Materials Science
User Authentication Mathematics
Support Vector Mathematics

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Research Output 2006 2019

  • 1017 Citations
  • 18 h-Index
  • 43 Article
  • 4 Conference contribution
  • 1 Comment/debate

Bin2Vec: A better wafer bin map coloring scheme for comprehensible visualization and effective bad wafer classification

Kim, J., Kim, H., Park, J., Mo, K. & Kang, P., 2019 Feb 11, In : Applied Sciences (Switzerland). 9, 3, 597.

Research output: Contribution to journalArticle

Open Access
19 Citations (Scopus)

Multi-co-training for document classification using various document representations: TF–IDF, LDA, and Doc2Vec

Kim, D., Seo, D., Cho, S. & Kang, P., 2019 Mar 1, In : Information Sciences. 477, p. 15-29 15 p.

Research output: Contribution to journalArticle

Document Classification
Semi-supervised Learning
Supervised learning
5 Citations (Scopus)

Recurrent inception convolution neural network for multi short-term load forecasting

Kim, J., Moon, J., Hwang, E. J. & Kang, P., 2019 Jul 1, In : Energy and Buildings. 194, p. 328-341 14 p.

Research output: Contribution to journalArticle

Recurrent neural networks
Electric load forecasting
Neural networks
Energy management systems
4 Citations (Scopus)

Recurrent neural network-based semantic variational autoencoder for Sequence-to-sequence learning

Jang, M., Seo, S. & Kang, P., 2019 Jul 1, In : Information Sciences. 490, p. 59-73 15 p.

Research output: Contribution to journalArticle

Recurrent neural networks
Recurrent Neural Networks
Natural Language
Language Modeling
1 Citation (Scopus)

Supervised paragraph vector: Distributed representations of words, documents and class labels

Park, E. L., Cho, S. & Kang, P., 2019 Jan 1, In : IEEE Access. 7, p. 29051-29064 14 p., 8653834.

Research output: Contribution to journalArticle

Computational efficiency
Principal component analysis