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Novel Semi-supervised Learning Models to Balance Data Inclusivity and Usability in Healthcare Applications

机译：新型半监督学习模型，用于平衡医疗保健应用中的数据包容性和可用性

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作者
Gaw, Nathan.;
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作者单位

Arizona State University.;

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授予单位 Arizona State University.;
学科
学位 Ph.D.
年度 2019
页码 164 p.
总页数 164
原文格式 PDF
正文语种 eng
中图分类
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2. Transductive active learning - A new semi-supervised learning approach based on iteratively refined generative models to capture structure in data [J] . Reitmaier Tobias, Calma Adrian, Sick Bernhard Information Sciences: An International Journal . 2015,第Null期

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5. Scalable and Data Efficient Deep Reinforcement Learning Methods for Healthcare Applications [D] . Saripalli, Venkata Ratnam. 2019

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6. Comparison of statistical and machine learning models for healthcare cost data: a simulation study motivated by Oncology Care Model (OCM) data [O] . Madhu Mazumdar, Jung-Yi Joyce Lin, Wei Zhang, 2020

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7. An Adaptive Neuro-Fuzzy System with Semi-Supervised Learning as an Approach to Improving Data Classification: An Illustration of Bad Debt Recovery in Healthcare [O] . Shi Donghui, Zurada Jozef, Guan Jian, 2015

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Novel Semi-supervised Learning Models to Balance Data Inclusivity and Usability in Healthcare Applications

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