Digital Library
Search: "[ keyword: Unsupervised Learning ]" (9)
Improved Focused Sampling for Class Imbalance Problem
Man Sun Kim , Hyung Jeong Yang , Soo Hyung Kim , Wooi Ping Cheah The KIPS Transactions:PartB ,
Vol. 14, No. 4, pp. 287-294,
Aug.
2007
10.3745/KIPSTB.2007.14.4.287
10.3745/KIPSTB.2007.14.4.287
Autoencoder Based N-Segmentation Frequency Domain Anomaly Detection for Optimization of Facility Defect Identification
Kichang Park, Yongkwan Lee The Transactions of the Korea Information Processing Society,
Vol. 13, No. 3, pp. 130-139,
Mar.
2024
https://doi.org/10.3745/TKIPS.2024.13.3.130
Keywords: Anomaly Detection, Prediction Maintenance, Autoencoder, Unsupervised learning, Frequency Domain
https://doi.org/10.3745/TKIPS.2024.13.3.130
Keywords: Anomaly Detection, Prediction Maintenance, Autoencoder, Unsupervised learning, Frequency Domain
Intrusion Detection Method Using Unsupervised Learning-Based Embedding and Autoencoder
Junwoo Lee, Kangseok Kim KIPS Transactions on Software and Data Engineering,
Vol. 12, No. 8, pp. 355-364,
Aug.
2023
https://doi.org/10.3745/KTSDE.2023.12.8.355
Keywords: Anomaly Detection, Unsupervised learning, Embedding Techniques, Autoencoder, Time-Series Data
https://doi.org/10.3745/KTSDE.2023.12.8.355
Keywords: Anomaly Detection, Unsupervised learning, Embedding Techniques, Autoencoder, Time-Series Data
Traffic Attributes Correlation Mechanism based on Self-Organizing Maps for Real-Time Intrusion Detection
Kyoung Ae Hwang , Ha Young Oh , Ji Young Lim , Ki Joon Chae , Jung Chan Nah The KIPS Transactions:PartC,
Vol. 12, No. 5, pp. 649-658,
Oct.
2005
10.3745/KIPSTC.2005.12.5.649
10.3745/KIPSTC.2005.12.5.649
Construction of Linearly Aliened Corpus Using Unsupervised Learning
Kong Joo Lee , Jae Hoon Kim The KIPS Transactions:PartB ,
Vol. 11, No. 3, pp. 387-394,
Jun.
2004
10.3745/KIPSTB.2004.11.3.387
10.3745/KIPSTB.2004.11.3.387
Network Intrusion Detection System Using Feature Extraction Based on AutoEncoder in IOT environment
Joohwa Lee, Keehyun Park KIPS Transactions on Software and Data Engineering,
Vol. 8, No. 12, pp. 483-490,
Dec.
2019
https://doi.org/10.3745/KTSDE.2019.8.12.483
Keywords: NIDS, IoT, Unsupervised learning, Machine Learning, Autoencoder
https://doi.org/10.3745/KTSDE.2019.8.12.483
Keywords: NIDS, IoT, Unsupervised learning, Machine Learning, Autoencoder
A Korean Language Stemmer based on Unsupervised Learning
Se Hyeong Cho The KIPS Transactions:PartB ,
Vol. 8, No. 6, pp. 675-684,
Dec.
2001
10.3745/KIPSTB.2001.8.6.675
10.3745/KIPSTB.2001.8.6.675
A Novel of Data Clustering Architecture for Outlier Detection to Electric Power Data Analysis
Se Hoon Jung, Chang Sun Shin, Young Yun Cho, Jang Woo Park, Myung Hye Park, Young Hyun Kim, Seung Bae Lee, Chun Bo Sim KIPS Transactions on Software and Data Engineering,
Vol. 6, No. 10, pp. 465-472,
Oct.
2017
10.3745/KTSDE.2017.6.10.465
Keywords: Data Analysis, Electric Power, Unsupervised Learning, Outlier, PCA
10.3745/KTSDE.2017.6.10.465
Keywords: Data Analysis, Electric Power, Unsupervised Learning, Outlier, PCA
Unsupervised Learning Model for Fault Prediction Using Representative Clustering Algorithms
Euy Seok Hong , Mi Kyeong Park KIPS Transactions on Software and Data Engineering,
Vol. 3, No. 2, pp. 57-64,
Feb.
2014
10.3745/KTSDE.2014.3.2.57
10.3745/KTSDE.2014.3.2.57