Integrative, Rapid, Data Analysis
Methods that bring together data from many sources and turn it into timely, usable insight.
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CiliateGEM: an open-project and a tool for predictions of ciliate metabolic variations and experimental condition design
A Mancini, F Eyassu, M Conway, A Occhipinti, P Lió, C Angione, S Pucciarelli. Cited by 5
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Domain Adaption via Feature Selection on Explicit Feature Map
W Deng, A Lendasse, YS Ong, IWH Tsang, L Chen, Q Zheng. Cited by 41
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Deep binary prototype multi-label learning
X Shen, W Liu, Y Luo, YS Ong, IWH Tsang. Cited by 3
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Trust-region based algorithms with low-budget for multi-objective optimization
PC Roy, J Blank, R Hussein, K Deb. Cited by 4
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Logistic Principle Component Analysis (L-PCA) for Feature Selection in Classification
J Rapeepongpan, P Padungweang, K Lavangnananda. Cited by 3
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Deep Discrete Prototype Multilabel Learning
X Shen, W Liu, Y Luo, YS Ong, IWH Tsang. Cited by 10
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Personalized Sequential Check-in Prediction: Beyond Geographical and Temporal Contexts
S Zhao, X Chen, I King, MRT Lyu. Cited by 7
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Co-evolutionary multi-task learning for dynamic time series prediction
R Chandra, YS Ong, CK Goh. Cited by 52
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Combining Pathway Identification and Breast Cancer Survival Prediction via Screening-Network Methods
A Iuliano, A Occhipinti, C Angelini, ID Feis, P Lió. Cited by 12
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Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regression
H Liu, J Cai, Y Wang, YS Ong. Cited by 23
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Compact Multi-Label Learning
X Shen, W Liu, IWH Tsang, Q Sun, YS Ong. Cited by 20
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GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs
J Zhang, X Shi, J Xie, H Ma, I King, D Yeung. Cited by 256