Discovery of early-alert indicators using hybrid ensemble learning and generative physics-based models

Author:

Yang Zhenyi1,Miao Rebecca1,Orlova Marina1,Nechepurenko Ivan2,Gavrishchaka Valeriy3

Affiliation:

1. Applied Quantitative Solutions for Complex Systems,Falls Church,VA,USA,22041

2. Moscow Institute of Physics and Technology Dolgoprudny,Moscow,Region Russian Federation

3. West Virginia University,Physics Department,Morgantown,WV,USA,26506

Publisher

IEEE

Reference25 articles.

1. Multi-expert evolving system for objective psychophysiological monitoring and fast discovery of effective personalized therapies

2. Discovery of Hybrid Ensemble Models Resilient to Input Resolution Deterioration

3. Deep learning;lecun;Nature,2015

4. Reducing the dimensionality of data with neural networks;hinton;Science,2006

5. Advantages of Hybrid Deep Learning Frameworks in Applications with Limited Data;gavrishchaka;International Journal of Machine Learning and Computing,2018

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Leveraging Neural-Networks, Boosting and Domain-Knowledge to Discover Physiological Indicators with Minimal Sensitivity to Data Resolution;2022 5th International Conference on Pattern Recognition and Artificial Intelligence (PRAI);2022-08-19

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