| An empirical study on modeling and prediction of bitcoin prices with bayesian neural networks based on blockchain information H Jang, J Lee IEEE access 6, 5427-5437, 2017 | 526 | 2017 |
| An improved cluster labeling method for support vector clustering J Lee, D Lee IEEE Transactions on pattern analysis and machine intelligence 27 (3), 461-464, 2005 | 356 | 2005 |
| Dynamic characterization of cluster structures for robust and inductive support vector clustering J Lee, D Lee IEEE Transactions on Pattern Analysis and Machine Intelligence 28 (11), 1869 …, 2006 | 167 | 2006 |
| Domain described support vector classifier for multi-classification problems D Lee, J Lee Pattern Recognition 40 (1), 41-51, 2007 | 135 | 2007 |
| Classification-based collaborative filtering using market basket data JS Lee, CH Jun, J Lee, S Kim Expert systems with applications 29 (3), 700-704, 2005 | 123 | 2005 |
| Improving memory-based collaborative filtering via similarity updating and prediction modulation B Jeong, J Lee, H Cho Information Sciences 180 (5), 602-612, 2010 | 116 | 2010 |
| Improved churn prediction in telecommunication industry by analyzing a large network K Kim, CH Jun, J Lee Expert Systems with Applications 41 (15), 6575-6584, 2014 | 115 | 2014 |
| A novel method for measuring semantic similarity for XML schema matching B Jeong, D Lee, H Cho, J Lee Expert Systems with Applications 34 (3), 1651-1658, 2008 | 113 | 2008 |
| Designing of variables repetitive group sampling plan involving minimum average sample number S Balamurali, H Park, CH Jun, KJ Kim, J Lee Communications in Statistics-Simulation and Computation 34 (3), 799-809, 2005 | 103 | 2005 |
| Predictability of machine learning techniques to forecast the trends of market index prices: Hypothesis testing for the Korean stock markets S Pyo, J Lee, M Cha, H Jang PloS one 12 (11), e0188107, 2017 | 97 | 2017 |
| Parametric models and non-parametric machine learning models for predicting option prices: Empirical comparison study over KOSPI 200 Index options H Park, N Kim, J Lee Expert Systems with Applications 41 (11), 5227-5237, 2014 | 95 | 2014 |
| A dynamical trajectory-based methodology for systematically computing multiple optimal solutions of general nonlinear programming problems J Lee, HD Chiang IEEE Transactions on Automatic Control 49 (6), 888-899, 2004 | 95 | 2004 |
| Understanding Catastrophic Overfitting in Single-step Adversarial Training H Kim, W Lee, J Lee AAAI 2021, 2020 | 93 | 2020 |
| Sentiment visualization and classification via semi-supervised nonlinear dimensionality reduction K Kim, J Lee Pattern Recognition 47 (2), 758-768, 2014 | 89 | 2014 |
| The economic value of NFT: Evidence from a portfolio analysis using mean–variance framework H Ko, B Son, Y Lee, H Jang, J Lee Finance Research Letters 47, 102784, 2022 | 88 | 2022 |
| Dynamic dissimilarity measure for support-based clustering D Lee, J Lee IEEE Transactions on Knowledge and Data Engineering 22 (6), 900-905, 2009 | 76 | 2009 |
| Equilibrium-based support vector machine for semisupervised classification D Lee, J Lee IEEE Transactions on Neural Networks 18 (2), 578-583, 2007 | 71 | 2007 |
| Clustering based on gaussian processes HC Kim, J Lee Neural computation 19 (11), 3088-3107, 2007 | 65 | 2007 |
| An iterative semi-explicit rating method for building collaborative recommender systems B Jeong, J Lee, H Cho Expert Systems with Applications 36 (3), 6181-6186, 2009 | 61 | 2009 |
| Do FOMC and macroeconomic announcements affect Bitcoin prices? S Pyo, J Lee Finance Research Letters 37, 101386, 2020 | 60 | 2020 |
Huisu JangFinance, Soongsil UniversityVerified email at ssu.ac.kr
