Integrating virtual sample generation with input-training neural network for solving small sample size problems: application to purified terephthalic acid solvent system
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Title
Integrating virtual sample generation with input-training neural network for solving small sample size problems: application to purified terephthalic acid solvent system
Authors
Keywords
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Journal
Soft Computing
Volume -, Issue -, Pages -
Publisher
Springer Science and Business Media LLC
Online
2021-02-27
DOI
10.1007/s00500-021-05641-4
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- (2017) Ignacio Martin-Diaz et al. IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
- Few-shot learning in deep networks through global prototyping
- (2017) Sebastian Blaes et al. NEURAL NETWORKS
- Mathematical interpolation methods for spatial estimation of global horizontal irradiation in Castilla-León, Spain: A case study
- (2017) M.C. Rodríguez-Amigo et al. SOLAR ENERGY
- SMURC: High-Dimension Small-Sample Multivariate Regression With Covariance Estimation
- (2017) Belhassen Bayar et al. IEEE Journal of Biomedical and Health Informatics
- A Survey of Predictive Modeling on Imbalanced Domains
- (2016) Paula Branco et al. ACM COMPUTING SURVEYS
- Integration of scheduling and control under uncertainties: Review and challenges
- (2016) Lisia S. Dias et al. CHEMICAL ENGINEERING RESEARCH & DESIGN
- SMOTE–IPF: Addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering
- (2015) José A. Sáez et al. INFORMATION SCIENCES
- A Projection Pursuit framework for supervised dimension reduction of high dimensional small sample datasets
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- (2014) Der-Chiang Li et al. DECISION SUPPORT SYSTEMS
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- (2011) Der-Chiang Li et al. EXPERT SYSTEMS WITH APPLICATIONS
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