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fuzzy bayesian network
相关语句
  模糊贝叶斯网络
     Application of fuzzy Bayesian network to trend prediction of silicon content in molten iron
     模糊贝叶斯网络应用于预测高炉铁水含硅量变化趋势
短句来源
  相似匹配句对
     Bayesian testing for fuzzy hypothesis of life characteristics
     寿命特征模糊假设的贝叶斯检验
短句来源
     A Bayesian Approach to Multiple Fuzzy Hypothesis Testing
     多重模糊假设检验的贝叶斯方法
短句来源
     M-N-Fuzzy Subgroup
     M-N-Fuzzy子群
短句来源
     L-Fuzzy H-Separation
     L-Fuzzy H-分离性
短句来源
     ve Bayesian.
     ve Bayesian的贝叶斯网络的模型。
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This study proposes a form of fuzzy Bayesian networks fusing continuous low-level image features and high-level semantics,which uses Gaussian mixture models(GMM) to make a fuzzy procedure.This particular procedure will transform continuous variables into discrete ones,when dealing with continuous inputs with probabilistic and uncertain nature,so that it can settle continuous inputs that discrete Bayesian networks can't handle.Moreover,it describes structure knowledge in fuzzy and...

This study proposes a form of fuzzy Bayesian networks fusing continuous low-level image features and high-level semantics,which uses Gaussian mixture models(GMM) to make a fuzzy procedure.This particular procedure will transform continuous variables into discrete ones,when dealing with continuous inputs with probabilistic and uncertain nature,so that it can settle continuous inputs that discrete Bayesian networks can't handle.Moreover,it describes structure knowledge in fuzzy and uncertain domain more reasonably.To demonstrate the validity of this method,we applied it to classification of astrocytoma malignant degree,and built a probabilistic model to predict astrocytoma malignant level.An accuracy of 83.33% is achieved out of testing 60 samples(30 benign and 30 malignant astrocytoma).It provides a novel objective method to quantitatively assess the astrocytoma malignant level that can be used to assist doctors to diagnose the tumor.

本研究提出一种新的融合影像低层视觉特征和语义的模糊贝叶斯网络模型,使用了高斯混合模型(GMM)对连续的视觉特征模糊化处理,解决了传统贝叶斯网络不能操作连续输入的问题,更合理地表达了具有模糊性、不确定性的专业领域的结构性知识。为了验证它的有效性,将它应用于星形细胞瘤恶性程度的分级,建立了一个概率模型,实验结果得出83.33%的正确识别率。该模型为星形细胞瘤恶性程度预测提供了新的定量而客观的辅助手段。

A modified fuzzy Bayesian network(FBN) is proposed in this study.It uses Gaussian mixture models(GMM) to make a fuzzy procedure for continuous image features.This particular procedure will transform continuous variables into discrete ones by soft quantizers,when dealing with continuous inputs with probabilistic and uncertain nature.It builds a hybrid Bayesian network(BN) construction modeling the causality of image features and diseases with expert knowledge,and trains the BN with data...

A modified fuzzy Bayesian network(FBN) is proposed in this study.It uses Gaussian mixture models(GMM) to make a fuzzy procedure for continuous image features.This particular procedure will transform continuous variables into discrete ones by soft quantizers,when dealing with continuous inputs with probabilistic and uncertain nature.It builds a hybrid Bayesian network(BN) construction modeling the causality of image features and diseases with expert knowledge,and trains the BN with data through machine learning,and estimates a probability of diseases by probability inference.This method is applied in prediction of the astrocytoma malignant degree and achieves an accuracy of 83.33%,which outperforms the BN using a crisp quantizer by a k-nearest neighbor classifier.This model provides more reasonable knowledge expression for domains with fuzzy and uncertain nature and a novel objective intelligent method to quantitatively assess the astrocytoma malignant level that can be used to assist doctors to diagnose the tumor.

针对医学影像特征具有模糊性和不确定性的特点,提出一种基于模糊贝叶斯网络的影像诊断预测模型。该模型使用高斯混合模型(GMM)对连续的视觉特征进行模糊量化处理,利用专家知识根据病症与影像特征之间的因果关系建立混合贝叶斯网络结构;由数据通过机器学习确定网络参数;采用概率推理定量估计病症的发生概率,从而建立一个可计算的预测模型。将该方法应用于星形细胞瘤分级预测,实验结果得出83.33%的正确识别率,远远超过使用最小近邻分类器(K-NN)实现连续变量硬(crisp)量化的贝叶斯网络模型,更合理地表达了具有模糊性、不确定性的专业领域的结构性知识,为星形细胞瘤恶性程度预测提供了新的辅助手段。

 
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