一种利用特权信息预测心血管事件的分类器

Proceedings of the 7th International Conference on PErvasive Technologies Related to Assistive Environments Pub Date : 2014-05-27 DOI:10.1145/2674396.2674434

G. Giannoulis, S. Yotov, M. Naghavi, M. Budoff, I. Kakadiaris

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摘要

使用特权信息学习(LUPI)是一种学习范例，旨在在训练期间(但不是在测试阶段)存在额外(特权)信息的情况下改进监督学习。例如，用于与心脏病相关的流行病学研究的动脉粥样硬化多种族研究(MESA)包含来自186个属性的数据，其中只有8个用于当前的风险预测算法。

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Towards a classifier for predicting cardiovascular events using privileged information

Learning Using Privileged Information (LUPI) is a learning paradigm that aims to improve supervised learning in the presence of additional (privileged) information available during training, but not during the testing phase. For example, the Multi-Ethnic Study of Atherosclerosis (MESA) used in epidemiological studies related to heart disease, contains data from 186 attributes, only eight of which are used in current risk prediction algorithms.

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Proceedings of the 7th International Conference on PErvasive Technologies Related to Assistive Environments

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