提出了基于反应和反应时间的能力与速度参数的两个联合模型M_Logistic和M_Probit,它们的反应部分的连接函数分别Logistic和Probit.在贝叶斯框架下,采用偏差信息准则(deviance information criterion,DIC)和对数伪边际似然(logarithm of ...提出了基于反应和反应时间的能力与速度参数的两个联合模型M_Logistic和M_Probit,它们的反应部分的连接函数分别Logistic和Probit.在贝叶斯框架下,采用偏差信息准则(deviance information criterion,DIC)和对数伪边际似然(logarithm of the pseudo marginal likelihood,LPML)进行模型比较.在实证研究中,采用M_Logistic,M_Probit,LNIRT对PISA数据进行拟合,结果显示其效果依次递减,模拟研究证实了本文的结论.展开更多
Image-based breast tumor classification is an active and challenging problem.In this paper,a robust breast tumor classification framework is presented based on deep feature representation learning and exploiting avail...Image-based breast tumor classification is an active and challenging problem.In this paper,a robust breast tumor classification framework is presented based on deep feature representation learning and exploiting available information in existing samples.Feature representation learning of mammograms is fulfilled by a modified nonnegative matrix factorization model called LPML-LRNMF,which is motivated by hierarchical learning and layer-wise pre-training(LP)strategy in deep learning.Low-rank(LR)constraint is integrated into the feature representation learning model by considering the intrinsic characteristics of mammograms.Moreover,the proposed LPML-LRNMF model is optimized via alternating direction method of multipliers and the corresponding convergence is analyzed.For completing classification,an inverse projection sparse representation model is introduced to exploit information embedded in existing samples,especially in test ones.Experiments on the public dataset and actual clinical dataset show that the classification accuracy,specificity and sensitivity achieve the clinical acceptance level.展开更多
针对国际学生评估项目2015年数据(PISA),采用单参数、双参数和三参数的项目反应模型进行建模,在每个模型下,分别采用logistic连接函数和probit连接函数。针对6个模型,应用偏差信息准则(Deviance Information Criterion,DIC)和伪边际似...针对国际学生评估项目2015年数据(PISA),采用单参数、双参数和三参数的项目反应模型进行建模,在每个模型下,分别采用logistic连接函数和probit连接函数。针对6个模型,应用偏差信息准则(Deviance Information Criterion,DIC)和伪边际似然对数(Logarithm of Pseudo-Marginal Likelihood,LPML)进行模型评价和模型选择。结果表明,当连接函数为logistic双参数的项目反应模型表现最好,因为这个模型下的DIC值最小,并且LPML值最大。我们采用R软件nimble包进行编程。展开更多
文摘提出了基于反应和反应时间的能力与速度参数的两个联合模型M_Logistic和M_Probit,它们的反应部分的连接函数分别Logistic和Probit.在贝叶斯框架下,采用偏差信息准则(deviance information criterion,DIC)和对数伪边际似然(logarithm of the pseudo marginal likelihood,LPML)进行模型比较.在实证研究中,采用M_Logistic,M_Probit,LNIRT对PISA数据进行拟合,结果显示其效果依次递减,模拟研究证实了本文的结论.
基金This work was supported in part by the National Natural Science Foundation of China(No.11701144)National Science Foundation of US(No.DMS1719932)+1 种基金Natural Science Foundation of Henan Province(No.162300410061)Project of Emerging Interdisciplinary(No.xxjc20170003).
文摘Image-based breast tumor classification is an active and challenging problem.In this paper,a robust breast tumor classification framework is presented based on deep feature representation learning and exploiting available information in existing samples.Feature representation learning of mammograms is fulfilled by a modified nonnegative matrix factorization model called LPML-LRNMF,which is motivated by hierarchical learning and layer-wise pre-training(LP)strategy in deep learning.Low-rank(LR)constraint is integrated into the feature representation learning model by considering the intrinsic characteristics of mammograms.Moreover,the proposed LPML-LRNMF model is optimized via alternating direction method of multipliers and the corresponding convergence is analyzed.For completing classification,an inverse projection sparse representation model is introduced to exploit information embedded in existing samples,especially in test ones.Experiments on the public dataset and actual clinical dataset show that the classification accuracy,specificity and sensitivity achieve the clinical acceptance level.
文摘针对国际学生评估项目2015年数据(PISA),采用单参数、双参数和三参数的项目反应模型进行建模,在每个模型下,分别采用logistic连接函数和probit连接函数。针对6个模型,应用偏差信息准则(Deviance Information Criterion,DIC)和伪边际似然对数(Logarithm of Pseudo-Marginal Likelihood,LPML)进行模型评价和模型选择。结果表明,当连接函数为logistic双参数的项目反应模型表现最好,因为这个模型下的DIC值最小,并且LPML值最大。我们采用R软件nimble包进行编程。
