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从随机集落影到随机点落影——隶属函数用于机器学习
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作者 汪培庄 鲁晨光 《智能系统学报》 北大核心 2025年第2期305-315,共11页
从样本分布求得隶属函数是重要的也是困难的。汪培庄的随机集落影理论使用集值统计得到隶属函数,从而在统计和模糊逻辑之间架起桥梁。但是,通常的样本并不包含集值,所以该理论不够实用。鲁晨光使用语义信息方法推导出用样本分布优化隶... 从样本分布求得隶属函数是重要的也是困难的。汪培庄的随机集落影理论使用集值统计得到隶属函数,从而在统计和模糊逻辑之间架起桥梁。但是,通常的样本并不包含集值,所以该理论不够实用。鲁晨光使用语义信息方法推导出用样本分布优化隶属函数的2个公式,它们和集值统计结果一致,可谓随机点落影方法。该方法可以用于多标签分类、最大互信息分类、混合模型、贝叶斯确证等。深度学习最新潮流中用的相似函数和估计互信息就是隶属函数和语义互信息的特例。因为最大语义信息准则和最大似然准则以及正则化最小误差平方准则兼容,并且隶属函数比似然函数迁移性更好,比反概率函数更容易构造,隶属函数有希望被广泛用于机器学习。 展开更多
关键词 模糊集合 隶属函数 样本分布 语义信息测度 机器学习 多标签分类 最大互信息分类 混合模型 贝叶斯确证
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Handling epistemic uncertainties in PRA using evidential networks
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作者 王冬 陈进 +1 位作者 程志君 郭波 《Journal of Central South University》 SCIE EI CAS 2014年第11期4261-4269,共9页
In order to overcome the limitations of traditional methods in uncertainty analysis, a modified Bayesian network(BN), which is called evidence network(EN), was proposed with evidence theory to handle epistemic uncerta... In order to overcome the limitations of traditional methods in uncertainty analysis, a modified Bayesian network(BN), which is called evidence network(EN), was proposed with evidence theory to handle epistemic uncertainty in probabilistic risk assessment(PRA). Fault trees(FTs) and event trees(ETs) were transformed into an EN which is used as a uniform framework to represent accident scenarios. Epistemic uncertainties of basic events in PRA were presented in evidence theory form and propagated through the network. A case study of a highway tunnel risk analysis was discussed to demonstrate the proposed approach. Frequencies of end states are obtained and expressed by belief and plausibility measures. The proposed approach addresses the uncertainties in experts' knowledge and can be easily applied to uncertainty analysis of FTs/ETs that have dependent events. 展开更多
关键词 probabilistic risk assessment epistemic uncertainty evidence theory evidential network
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