ScholarWorks@Hanyang University: Lee, Ki chun

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Lee, Ki chun (이기천)

COLLEGE OF ENGINEERING (DEPARTMENT OF INDUSTRIAL ENGINEERING)

Research Area: 산업공학 및 통계

Tel 02-2220-0478
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Clustering:4||Feature selection:2||Community structure:2||Ensemble:2||Boosting:2||Markov chain:2||Orthogonal signal correction:2||LSTM:2||metabolomics:2||C88:1||Cell formation:1||Class association rules:1||Collaborative processing:1||Cumulative evolutionary slope:1||Data mining:1||Denoising:1||Dependence:1||Dependence maps:1||Diffusion:1||Dimensionality reduction:1||Domain adaptation:1||Dual:1||Ensemble Learning:1||Exons:1||Fractional Brownian motion:1||GT efficacy:1||Generalized extreme studentized deviate (GESD) test:1||Group dependence:1||Hidden Markov models:1||Hurst exponent:1||Image classification:1||Introns:1||Knowledge distillation:1||Language model:1||Large Data Metamodeling:1||Learning effect:1||MCTS:1||Manifoldregularization:1||Markovian:1||Maximumlikelihood:1||Metabolomics:1||Model selection:1||Modularity:1||Multiscale analysis:1||Mutual information:1||Nearinfrared spectroscopy:1||Network compression:1||Neural network compression:1||Nonconvex optimization:1||Oneclass classification:1||Opinion mining:1||Ordinal data:1||Outlier detection:1||PLS:1||Parallel computing:1||Parameter selection:1||Partial linear dependence:1||Performance measure:1||Permutation test:1||Principal component analysis:1||Product transition:1||RNN:1||Rampdown production:1||Rampup production:1||Recommendation systems:1||Regression:1||Regression vector:1||Regularity:1||Rule pruning:1||Rule ranking:1||Sampling techniques:1||Semisupervised learning:1||Sentiment analysis:1||Social network:1||Sparse coding:1||Streaming data:1||Structured pruning:1||Sustainability:1||Time series:1||Trust prediction:1||Trust propagation:1||Trustmessage passing:1||Turbulence signals:1||Unstructured pruning:1||Variance estimation:1||Wavelet shrinkage:1||Wavelets:1||Weight pruning:1||acute lung injury:1||adaptive boosting:1||air pollution quality:1||albumin:1||anomaly detection:1||application performance monitoring:1||association rules:1||bile acid:1||buffering scheme:1||cation binders:1||cell formation:1||colloid liquid crystals:1||crossselling:1||data sparsity:1||database system:1||decision tree:1||deep reinforcement learning:1||dependence clustering:1||dimensionality reduction:1||dynamic regression:1||dynamic transfer:1||ensemble:1||ensemble technique:1||feature selection:1||financial distress prediction:1||flash storage:1||genetic algorithm:1||graphene oxide:1||graphene oxide fibers:1||human plasma:1||hybrid genetic algorithm:1||imputation:1||instance selection:1||item assignment:1||largescale dataset:1||lung transplantation:1||natural language processing:1||neural networks:1||oneclass support vector machine:1||online data analysis:1||ordinal classification:1||ordinal data:1||outlier detection:1||performance measures:1||proton nuclear magnetic resonance:1||rainfall radar images:1||recovery:1||reinforcement learning:1||relative support distance:1||scale invariance:1||support vector candidates:1||support vector machine:1||support vector machines:1||symbolic aggregate approximation:1||text mining:1||timeseries representation:1||transformer:1||transition information:1||trust networks:1||unsupervised learning:1||variational autoencoder:1||vehicle routing problem:1||Alternative direction method of multipliers (ADMM):1||wavelet transform:1||Ambiguous edge:1||Anomaly detection:1||Aspiration:1||Associative classification:1||Bayesian estimation:1||Biplot correlation:1||C14:1||C18:1||

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